Abstract

Automated segmentation of object boundaries or surfaces is crucial for quantitative image analysis in numerous biomedical applications. For example, retinal surfaces in optical coherence tomography (OCT) images play a vital role in the diagnosis and management of retinal diseases. Recently, graph based surface segmentation and contour modeling have been developed and optimized for various surface segmentation tasks. These methods require expertly designed, application specific transforms, including cost functions, constraints and model parameters. However, deep learning based methods are able to directly learn the model and features from training data. In this paper, we propose a convolutional neural network (CNN) based framework to segment multiple surfaces simultaneously. We demonstrate the application of the proposed method by training a single CNN to segment three retinal surfaces in two types of OCT images - normal retinas and retinas affected by intermediate age-related macular degeneration (AMD). The trained network directly infers the segmentations for each B-scan in one pass. The proposed method was validated on 50 retinal OCT volumes (3000 B-scans) including 25 normal and 25 intermediate AMD subjects. Our experiment demonstrated statistically significant improvement of segmentation accuracy compared to the optimal surface segmentation method with convex priors (OSCS) and two deep learning based UNET methods for both types of data. The average computation time for segmenting an entire OCT volume (consisting of 60 B-scans each) for the proposed method was 12.3 seconds, demonstrating low computation costs and higher performance compared to the graph based optimal surface segmentation and UNET based methods.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
    [Crossref] [PubMed]
  2. A. P. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
    [Crossref]
  3. A. Shah, J. Bai, Z Hu, S. Sadda, and X. Wu, “Multiple Surface Segmentation Using Truncated Convex Priors,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 97–104.
  4. J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS one 10(8), e0133908 (2015).
    [Crossref] [PubMed]
  5. S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428(2015).
    [Crossref]
  6. Y. Boykov and G. Funka-Lea, “Graph cuts and efficient and image segmentation,” Int. J. Comp. Vis. 70(2), 109–131 (2006).
    [Crossref]
  7. A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intraretinal layer segmentation in optical coherence tomography using an active contour approach,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2009), pp. 649–656.
  8. S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based CV model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
    [Crossref] [PubMed]
  9. L. de Sisternes, G. Jonna, J. Moss, M. F. Marmor, T. Leng, and D. L. Rubin, “Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes,” Biomed. Opt. Express 8(3), 1926–1949 (2017).
    [Crossref] [PubMed]
  10. A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
    [Crossref] [PubMed]
  11. B. J. Antony, M. D. Abrámoff, M. M. Harper, W. Jeong, E. H. Sohn, Y. H. Kwon, R. Kardon, and M. K. Garvin, “A combined machine learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes,” Biomed. Opt. Express 4(12), 2712–2728 (2013).
    [Crossref]
  12. Z. Ma, J. M. R. Tavares, and R. N. Jorge, “A review on the current segmentation algorithms for medical images,” in Proceedings of the 1st International Conference on Imaging Theory and Applications (2009).
  13. R. Kafieh, H. Rabbani, and S. Kermani, “A review of algorithms for segmentation of optical coherence tomography from retina,” Journal of Medical Signals and Sensors 3(1), 45 (2013).
    [PubMed]
  14. S. Kashyap, Y. Yin, and M. Sonka, “Automated analysis of cartilage morphology,” in International Symposium on Biomedical Imaging (IEEE, 2013), pp. 1300–1303.
  15. Y. Yin, X. Zhang, R. Williams, X. Wu, D. D. Anderson, and M. Sonka, “Logismos layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint,” IEEE Trans. Med. Imag. 29(12), 2023–2037 (2010).
    [Crossref]
  16. D. J. Withey and Z. J. Koles, “A review of medical image segmentation: methods and available software,” International Journal of Bioelectromagnetism 10(3), 125–148 (2008).
  17. X. Liu, D. Z. Chen, M. H. Tawhai, X. Wu, E. A. Hoffman, and M. Sonka, “Optimal graph search based segmentation of airway tree double surfaces across bifurcations,” IEEE Trans. Med. Imag. 32(3), 493–510 (2013).
    [Crossref]
  18. C. Bauer, M. A. Krueger, W. J. Lamm, B. J. Smith, R. W. Glenny, and R. R. Beichel, “Airway tree segmentation in serial block-face cryomicrotome images of rat lungs,” IEEE Trans. Biomed. Eng. 61(1), 119–130 (2014).
    [Crossref]
  19. S. Sun, M. Sonka, and R. R. Beichel, “Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface,” Computerized Medical Imaging and Graphics,  37(1), 15–27 (2013).
    [Crossref] [PubMed]
  20. X. Zhang, J. Tian, K. Deng, Y. Wu, and X. Li, “Automatic liver segmentation using a statistical shape model with optimal surface detection,” IEEE Trans. Biomed. Eng. 57(10), 2622–2626 (2010).
    [Crossref] [PubMed]
  21. K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abrámoff, “Segmentation of the optic disc in 3-d oct scans of the optic nerve head,” IEEE Trans. Med. Imag. 29(1), 159–168 (2010).
    [Crossref]
  22. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
    [Crossref] [PubMed]
  23. E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
    [Crossref]
  24. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition (IEEE, 2016), pp. 770–778.
  25. H. Greenspan, B. van Ginneken, and R. M. Summers, “Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,” IEEE Trans. Med. Imag. 35(5), 1153–1159 (2016).
    [Crossref]
  26. M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
    [Crossref]
  27. S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate mr segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2013), pp. 254–261.
  28. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), 1097–1105.
  29. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 234–241.
  30. K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
    [Crossref]
  31. P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.
  32. R. Korez, B. Likar, F. Pernus, and T. Vrtovec, “Model-based segmentation of vertebral bodies from MR images with 3D CNNs,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), pp. 433–441.
  33. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
    [Crossref] [PubMed]
  34. J. Tian, B. Varga, E. Tatrai, P. Fanni, G. Somfai, W. Smiddy, and D. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophoton. 1(9), 478–489 (2016).
    [Crossref]
  35. M. D. Abrámoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
    [Crossref] [PubMed]
  36. N. M. Bressler, “Age-related macular degeneration is the leading cause of blindness,” JAMA 291(15), 1900–1901 (2004).
    [Crossref] [PubMed]
  37. M. K. Garvin, M. D. Abrámoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009).
    [Crossref]
  38. F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
    [Crossref]
  39. Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag. 32(2), 376–386 (2013).
    [Crossref]
  40. A. Shah, J.-K. Wang, M. K. Garvin, M. Sonka, and X. Wu, “Automated surface segmentation of internal limiting membrane in spectral-domain optical coherence tomography volumes with a deep cup using a 3D range expansion approach,” in International Symposium on Biomedical Imaging (IEEE, 2014), pp. 1405–1408.
  41. L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
    [Crossref] [PubMed]
  42. M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in edi-oct images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis (Springer, 2017), pp. 177–184.
    [Crossref]
  43. X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
    [Crossref]
  44. F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
    [Crossref]
  45. A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network,” arXiv preprint 1704.02161 (2017).
  46. O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer- Assisted Intervention, (Springer, 2016), pp. 424–432.
  47. A. Shah, M. D. Abramoff, and X. Wu, “Simultaneous multiple surface segmentation using deep learning,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 3–11.
    [Crossref]
  48. S. Farsiu, S. J. Chiu, R. V. O Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmol. 121(1), pp. 162–172 (2014).
    [Crossref]
  49. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint 1408.5093 (2014).
  50. D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint 1412.6980, (2014).
  51. Q. Song, X. Wu, Y. Liu, M. Smith, J. Buatti, and M. Sonka, “Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2009), pp. 827–835.
  52. S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” arXiv preprint 1502.03167, (2015).

2017 (7)

L. de Sisternes, G. Jonna, J. Moss, M. F. Marmor, T. Leng, and D. L. Rubin, “Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes,” Biomed. Opt. Express 8(3), 1926–1949 (2017).
[Crossref] [PubMed]

E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref]

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
[Crossref]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref] [PubMed]

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
[Crossref]

F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
[Crossref]

2016 (3)

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. Somfai, W. Smiddy, and D. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophoton. 1(9), 478–489 (2016).
[Crossref]

H. Greenspan, B. van Ginneken, and R. M. Summers, “Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,” IEEE Trans. Med. Imag. 35(5), 1153–1159 (2016).
[Crossref]

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based CV model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
[Crossref] [PubMed]

2015 (4)

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS one 10(8), e0133908 (2015).
[Crossref] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428(2015).
[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
[Crossref]

2014 (2)

S. Farsiu, S. J. Chiu, R. V. O Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmol. 121(1), pp. 162–172 (2014).
[Crossref]

C. Bauer, M. A. Krueger, W. J. Lamm, B. J. Smith, R. W. Glenny, and R. R. Beichel, “Airway tree segmentation in serial block-face cryomicrotome images of rat lungs,” IEEE Trans. Biomed. Eng. 61(1), 119–130 (2014).
[Crossref]

2013 (7)

S. Sun, M. Sonka, and R. R. Beichel, “Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface,” Computerized Medical Imaging and Graphics,  37(1), 15–27 (2013).
[Crossref] [PubMed]

X. Liu, D. Z. Chen, M. H. Tawhai, X. Wu, E. A. Hoffman, and M. Sonka, “Optimal graph search based segmentation of airway tree double surfaces across bifurcations,” IEEE Trans. Med. Imag. 32(3), 493–510 (2013).
[Crossref]

A. P. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[Crossref] [PubMed]

B. J. Antony, M. D. Abrámoff, M. M. Harper, W. Jeong, E. H. Sohn, Y. H. Kwon, R. Kardon, and M. K. Garvin, “A combined machine learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes,” Biomed. Opt. Express 4(12), 2712–2728 (2013).
[Crossref]

R. Kafieh, H. Rabbani, and S. Kermani, “A review of algorithms for segmentation of optical coherence tomography from retina,” Journal of Medical Signals and Sensors 3(1), 45 (2013).
[PubMed]

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag. 32(2), 376–386 (2013).
[Crossref]

2010 (4)

M. D. Abrámoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

Y. Yin, X. Zhang, R. Williams, X. Wu, D. D. Anderson, and M. Sonka, “Logismos layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint,” IEEE Trans. Med. Imag. 29(12), 2023–2037 (2010).
[Crossref]

X. Zhang, J. Tian, K. Deng, Y. Wu, and X. Li, “Automatic liver segmentation using a statistical shape model with optimal surface detection,” IEEE Trans. Biomed. Eng. 57(10), 2622–2626 (2010).
[Crossref] [PubMed]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abrámoff, “Segmentation of the optic disc in 3-d oct scans of the optic nerve head,” IEEE Trans. Med. Imag. 29(1), 159–168 (2010).
[Crossref]

2009 (1)

M. K. Garvin, M. D. Abrámoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009).
[Crossref]

2008 (1)

D. J. Withey and Z. J. Koles, “A review of medical image segmentation: methods and available software,” International Journal of Bioelectromagnetism 10(3), 125–148 (2008).

2006 (2)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

Y. Boykov and G. Funka-Lea, “Graph cuts and efficient and image segmentation,” Int. J. Comp. Vis. 70(2), 109–131 (2006).
[Crossref]

2004 (1)

N. M. Bressler, “Age-related macular degeneration is the leading cause of blindness,” JAMA 291(15), 1900–1901 (2004).
[Crossref] [PubMed]

1991 (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Abdillahi, H.

A. P. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Abdulkadir, A.

O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer- Assisted Intervention, (Springer, 2016), pp. 424–432.

Abramoff, M. D.

A. Shah, M. D. Abramoff, and X. Wu, “Simultaneous multiple surface segmentation using deep learning,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 3–11.
[Crossref]

Abrámoff, M. D.

B. J. Antony, M. D. Abrámoff, M. M. Harper, W. Jeong, E. H. Sohn, Y. H. Kwon, R. Kardon, and M. K. Garvin, “A combined machine learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes,” Biomed. Opt. Express 4(12), 2712–2728 (2013).
[Crossref]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abrámoff, “Segmentation of the optic disc in 3-d oct scans of the optic nerve head,” IEEE Trans. Med. Imag. 29(1), 159–168 (2010).
[Crossref]

M. D. Abrámoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

M. K. Garvin, M. D. Abrámoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009).
[Crossref]

Anderson, D. D.

Y. Yin, X. Zhang, R. Williams, X. Wu, D. D. Anderson, and M. Sonka, “Logismos layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint,” IEEE Trans. Med. Imag. 29(12), 2023–2037 (2010).
[Crossref]

Antony, B. J.

Armbruster, M.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

Ba, J.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint 1412.6980, (2014).

Bai, J.

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag. 32(2), 376–386 (2013).
[Crossref]

A. Shah, J. Bai, Z Hu, S. Sadda, and X. Wu, “Multiple Surface Segmentation Using Truncated Convex Priors,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 97–104.

Bauer, C.

C. Bauer, M. A. Krueger, W. J. Lamm, B. J. Smith, R. W. Glenny, and R. R. Beichel, “Airway tree segmentation in serial block-face cryomicrotome images of rat lungs,” IEEE Trans. Biomed. Eng. 61(1), 119–130 (2014).
[Crossref]

Beichel, R. R.

C. Bauer, M. A. Krueger, W. J. Lamm, B. J. Smith, R. W. Glenny, and R. R. Beichel, “Airway tree segmentation in serial block-face cryomicrotome images of rat lungs,” IEEE Trans. Biomed. Eng. 61(1), 119–130 (2014).
[Crossref]

S. Sun, M. Sonka, and R. R. Beichel, “Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface,” Computerized Medical Imaging and Graphics,  37(1), 15–27 (2013).
[Crossref] [PubMed]

Bengio, Y.

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Bi, H.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
[Crossref]

Biard, A.

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

Bickel, M.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

Bilic, P.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

Boykov, Y.

Y. Boykov and G. Funka-Lea, “Graph cuts and efficient and image segmentation,” Int. J. Comp. Vis. 70(2), 109–131 (2006).
[Crossref]

Bressler, N. M.

N. M. Bressler, “Age-related macular degeneration is the leading cause of blindness,” JAMA 291(15), 1900–1901 (2004).
[Crossref] [PubMed]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 234–241.

O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer- Assisted Intervention, (Springer, 2016), pp. 424–432.

Buatti, J.

Q. Song, X. Wu, Y. Liu, M. Smith, J. Buatti, and M. Sonka, “Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2009), pp. 827–835.

Buatti, J. M.

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag. 32(2), 376–386 (2013).
[Crossref]

Burns, T. L.

M. K. Garvin, M. D. Abrámoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009).
[Crossref]

Calabresi, P. A.

Carass, A.

Ceklic, L.

A. P. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Chen, D. Z.

X. Liu, D. Z. Chen, M. H. Tawhai, X. Wu, E. A. Hoffman, and M. Sonka, “Optimal graph search based segmentation of airway tree double surfaces across bifurcations,” IEEE Trans. Med. Imag. 32(3), 493–510 (2013).
[Crossref]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

Chen, H.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
[Crossref]

Chen, M.

M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in edi-oct images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis (Springer, 2017), pp. 177–184.
[Crossref]

Chen, Q.

Chen, X.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
[Crossref]

Chiu, S. J.

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428(2015).
[Crossref]

S. Farsiu, S. J. Chiu, R. V. O Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmol. 121(1), pp. 162–172 (2014).
[Crossref]

Christ, P. F.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

Cicek, O.

O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer- Assisted Intervention, (Springer, 2016), pp. 424–432.

Conjeti, S.

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network,” arXiv preprint 1704.02161 (2017).

Connell, R. V. O

S. Farsiu, S. J. Chiu, R. V. O Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmol. 121(1), pp. 162–172 (2014).
[Crossref]

Courville, A.

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

Cunefare, D.

DAnastasi, M.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

Darrell, T.

E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref]

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint 1408.5093 (2014).

Davy, A.

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

de Sisternes, L.

Debuc, D.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. Somfai, W. Smiddy, and D. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophoton. 1(9), 478–489 (2016).
[Crossref]

DeBuc, D. C.

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS one 10(8), e0133908 (2015).
[Crossref] [PubMed]

Deng, K.

X. Zhang, J. Tian, K. Deng, Y. Wu, and X. Li, “Automatic liver segmentation using a statistical shape model with optimal surface detection,” IEEE Trans. Biomed. Eng. 57(10), 2622–2626 (2010).
[Crossref] [PubMed]

Donahue, J.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint 1408.5093 (2014).

Dufour, A. P.

A. P. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Dzanet, S. D.

A. P. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Elshaer, M. E. A.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

Ettlinger, F.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

Fang, L.

Fanni, P.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. Somfai, W. Smiddy, and D. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophoton. 1(9), 478–489 (2016).
[Crossref]

Farsiu, S.

Fauser, S.

F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
[Crossref]

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 234–241.

Flotte, T.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Folgar, F. A.

S. Farsiu, S. J. Chiu, R. V. O Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmol. 121(1), pp. 162–172 (2014).
[Crossref]

Funka-Lea, G.

Y. Boykov and G. Funka-Lea, “Graph cuts and efficient and image segmentation,” Int. J. Comp. Vis. 70(2), 109–131 (2006).
[Crossref]

Gao, E.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
[Crossref]

Gao, Y.

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate mr segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2013), pp. 254–261.

Garvin, M. K.

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag. 32(2), 376–386 (2013).
[Crossref]

B. J. Antony, M. D. Abrámoff, M. M. Harper, W. Jeong, E. H. Sohn, Y. H. Kwon, R. Kardon, and M. K. Garvin, “A combined machine learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes,” Biomed. Opt. Express 4(12), 2712–2728 (2013).
[Crossref]

M. D. Abrámoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abrámoff, “Segmentation of the optic disc in 3-d oct scans of the optic nerve head,” IEEE Trans. Med. Imag. 29(1), 159–168 (2010).
[Crossref]

M. K. Garvin, M. D. Abrámoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009).
[Crossref]

A. Shah, J.-K. Wang, M. K. Garvin, M. Sonka, and X. Wu, “Automated surface segmentation of internal limiting membrane in spectral-domain optical coherence tomography volumes with a deep cup using a 3D range expansion approach,” in International Symposium on Biomedical Imaging (IEEE, 2014), pp. 1405–1408.

Gee, J. C.

M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in edi-oct images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis (Springer, 2017), pp. 177–184.
[Crossref]

Ginneken, B.

F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
[Crossref]

Girshick, R.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint 1408.5093 (2014).

Glenny, R. W.

C. Bauer, M. A. Krueger, W. J. Lamm, B. J. Smith, R. W. Glenny, and R. R. Beichel, “Airway tree segmentation in serial block-face cryomicrotome images of rat lungs,” IEEE Trans. Biomed. Eng. 61(1), 119–130 (2014).
[Crossref]

Glocker, B.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
[Crossref]

Greenspan, H.

H. Greenspan, B. van Ginneken, and R. M. Summers, “Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,” IEEE Trans. Med. Imag. 35(5), 1153–1159 (2016).
[Crossref]

Gregory, K.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Grinsven, M.

F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
[Crossref]

Guadarrama, S.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint 1408.5093 (2014).

Guymer, R. H.

Hamarneh, G.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intraretinal layer segmentation in optical coherence tomography using an active contour approach,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2009), pp. 649–656.

Harper, M. M.

Hauser, M.

Havaei, M.

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition (IEEE, 2016), pp. 770–778.

Hee, M. R.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), 1097–1105.

Hoffman, E. A.

X. Liu, D. Z. Chen, M. H. Tawhai, X. Wu, E. A. Hoffman, and M. Sonka, “Optimal graph search based segmentation of airway tree double surfaces across bifurcations,” IEEE Trans. Med. Imag. 32(3), 493–510 (2013).
[Crossref]

Hofmann, F.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

Hoyng, C.

F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
[Crossref]

Hu, Z

A. Shah, J. Bai, Z Hu, S. Sadda, and X. Wu, “Multiple Surface Segmentation Using Truncated Convex Priors,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 97–104.

Huang, D.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Ioffe, S.

S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” arXiv preprint 1502.03167, (2015).

Izatt, J. A.

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428(2015).
[Crossref]

S. Farsiu, S. J. Chiu, R. V. O Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmol. 121(1), pp. 162–172 (2014).
[Crossref]

Jeong, W.

Jia, Y.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint 1408.5093 (2014).

Jodoin, P.-M.

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

Jonna, G.

Jorge, R. N.

Z. Ma, J. M. R. Tavares, and R. N. Jorge, “A review on the current segmentation algorithms for medical images,” in Proceedings of the 1st International Conference on Imaging Theory and Applications (2009).

Kafieh, R.

R. Kafieh, H. Rabbani, and S. Kermani, “A review of algorithms for segmentation of optical coherence tomography from retina,” Journal of Medical Signals and Sensors 3(1), 45 (2013).
[PubMed]

Kamnitsas, K.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
[Crossref]

Kane, A. D.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
[Crossref]

Karayev, S.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint 1408.5093 (2014).

Kardon, R.

Karri, S. P. K.

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network,” arXiv preprint 1704.02161 (2017).

Kashyap, S.

S. Kashyap, Y. Yin, and M. Sonka, “Automated analysis of cartilage morphology,” in International Symposium on Biomedical Imaging (IEEE, 2013), pp. 1300–1303.

Katouzian, A.

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network,” arXiv preprint 1704.02161 (2017).

Kermani, S.

R. Kafieh, H. Rabbani, and S. Kermani, “A review of algorithms for segmentation of optical coherence tomography from retina,” Journal of Medical Signals and Sensors 3(1), 45 (2013).
[PubMed]

Kingma, D.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint 1412.6980, (2014).

Koles, Z. J.

D. J. Withey and Z. J. Koles, “A review of medical image segmentation: methods and available software,” International Journal of Bioelectromagnetism 10(3), 125–148 (2008).

Korez, R.

R. Korez, B. Likar, F. Pernus, and T. Vrtovec, “Model-based segmentation of vertebral bodies from MR images with 3D CNNs,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), pp. 433–441.

Kowal, J.

A. P. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), 1097–1105.

Krueger, M. A.

C. Bauer, M. A. Krueger, W. J. Lamm, B. J. Smith, R. W. Glenny, and R. R. Beichel, “Airway tree segmentation in serial block-face cryomicrotome images of rat lungs,” IEEE Trans. Biomed. Eng. 61(1), 119–130 (2014).
[Crossref]

Kwon, Y. H.

B. J. Antony, M. D. Abrámoff, M. M. Harper, W. Jeong, E. H. Sohn, Y. H. Kwon, R. Kardon, and M. K. Garvin, “A combined machine learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes,” Biomed. Opt. Express 4(12), 2712–2728 (2013).
[Crossref]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abrámoff, “Segmentation of the optic disc in 3-d oct scans of the optic nerve head,” IEEE Trans. Med. Imag. 29(1), 159–168 (2010).
[Crossref]

Lamm, W. J.

C. Bauer, M. A. Krueger, W. J. Lamm, B. J. Smith, R. W. Glenny, and R. R. Beichel, “Airway tree segmentation in serial block-face cryomicrotome images of rat lungs,” IEEE Trans. Biomed. Eng. 61(1), 119–130 (2014).
[Crossref]

Lang, A.

Larochelle, H.

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

LeCun, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Ledig, C.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
[Crossref]

Lee, K.

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abrámoff, “Segmentation of the optic disc in 3-d oct scans of the optic nerve head,” IEEE Trans. Med. Imag. 29(1), 159–168 (2010).
[Crossref]

Lee, W.-H.

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS one 10(8), e0133908 (2015).
[Crossref] [PubMed]

Leng, T.

Li, K.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

Li, S.

Li, X.

X. Zhang, J. Tian, K. Deng, Y. Wu, and X. Li, “Automatic liver segmentation using a statistical shape model with optimal surface detection,” IEEE Trans. Biomed. Eng. 57(10), 2622–2626 (2010).
[Crossref] [PubMed]

Li, X. T.

Liao, S.

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate mr segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2013), pp. 254–261.

Liefers, B.

F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
[Crossref]

Lienkamp, S. S.

O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer- Assisted Intervention, (Springer, 2016), pp. 424–432.

Likar, B.

R. Korez, B. Likar, F. Pernus, and T. Vrtovec, “Model-based segmentation of vertebral bodies from MR images with 3D CNNs,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), pp. 433–441.

Lin, C. P.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Liu, X.

X. Liu, D. Z. Chen, M. H. Tawhai, X. Wu, E. A. Hoffman, and M. Sonka, “Optimal graph search based segmentation of airway tree double surfaces across bifurcations,” IEEE Trans. Med. Imag. 32(3), 493–510 (2013).
[Crossref]

Liu, Y.

Q. Song, X. Wu, Y. Liu, M. Smith, J. Buatti, and M. Sonka, “Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2009), pp. 827–835.

Long, J.

E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref]

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint 1408.5093 (2014).

Ma, Z.

Z. Ma, J. M. R. Tavares, and R. N. Jorge, “A review on the current segmentation algorithms for medical images,” in Proceedings of the 1st International Conference on Imaging Theory and Applications (2009).

Marmor, M. F.

Menon, D. K.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
[Crossref]

Moss, J.

Navab, N.

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network,” arXiv preprint 1704.02161 (2017).

Newcombe, V. F.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
[Crossref]

Nicholas, P.

Niemeijer, M.

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abrámoff, “Segmentation of the optic disc in 3-d oct scans of the optic nerve head,” IEEE Trans. Med. Imag. 29(1), 159–168 (2010).
[Crossref]

Niu, S.

Oguz, I.

M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in edi-oct images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis (Springer, 2017), pp. 177–184.
[Crossref]

Oto, A.

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate mr segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2013), pp. 254–261.

Pal, C.

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

Pan, X.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
[Crossref]

Pernus, F.

R. Korez, B. Likar, F. Pernus, and T. Vrtovec, “Model-based segmentation of vertebral bodies from MR images with 3D CNNs,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), pp. 433–441.

Prince, J. L.

Puliafito, C. A.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Rabbani, H.

R. Kafieh, H. Rabbani, and S. Kermani, “A review of algorithms for segmentation of optical coherence tomography from retina,” Journal of Medical Signals and Sensors 3(1), 45 (2013).
[PubMed]

Rempfier, M.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition (IEEE, 2016), pp. 770–778.

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 234–241.

O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer- Assisted Intervention, (Springer, 2016), pp. 424–432.

Roy, A. G.

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network,” arXiv preprint 1704.02161 (2017).

Rubin, D. L.

Rueckert, D.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
[Crossref]

Russell, S. R.

M. K. Garvin, M. D. Abrámoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009).
[Crossref]

Sadda, S.

A. Shah, J. Bai, Z Hu, S. Sadda, and X. Wu, “Multiple Surface Segmentation Using Truncated Convex Priors,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 97–104.

Sanchez, C.

F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
[Crossref]

Sarunic, M.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intraretinal layer segmentation in optical coherence tomography using an active contour approach,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2009), pp. 649–656.

Schroder, S.

A. P. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Schuman, J. S.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Shah, A.

A. Shah, J.-K. Wang, M. K. Garvin, M. Sonka, and X. Wu, “Automated surface segmentation of internal limiting membrane in spectral-domain optical coherence tomography volumes with a deep cup using a 3D range expansion approach,” in International Symposium on Biomedical Imaging (IEEE, 2014), pp. 1405–1408.

A. Shah, J. Bai, Z Hu, S. Sadda, and X. Wu, “Multiple Surface Segmentation Using Truncated Convex Priors,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 97–104.

A. Shah, M. D. Abramoff, and X. Wu, “Simultaneous multiple surface segmentation using deep learning,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 3–11.
[Crossref]

Sheet, D.

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network,” arXiv preprint 1704.02161 (2017).

Shelhamer, E.

E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref]

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint 1408.5093 (2014).

Shen, D.

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate mr segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2013), pp. 254–261.

Shi, F.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
[Crossref]

Simpson, J. P.

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
[Crossref]

Smiddy, W.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. Somfai, W. Smiddy, and D. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophoton. 1(9), 478–489 (2016).
[Crossref]

Smiddy, W. E.

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS one 10(8), e0133908 (2015).
[Crossref] [PubMed]

Smith, B.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intraretinal layer segmentation in optical coherence tomography using an active contour approach,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2009), pp. 649–656.

Smith, B. J.

C. Bauer, M. A. Krueger, W. J. Lamm, B. J. Smith, R. W. Glenny, and R. R. Beichel, “Airway tree segmentation in serial block-face cryomicrotome images of rat lungs,” IEEE Trans. Biomed. Eng. 61(1), 119–130 (2014).
[Crossref]

Smith, M.

Q. Song, X. Wu, Y. Liu, M. Smith, J. Buatti, and M. Sonka, “Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2009), pp. 827–835.

Sohn, E. H.

Somfai, G.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. Somfai, W. Smiddy, and D. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophoton. 1(9), 478–489 (2016).
[Crossref]

Somfai, G. M.

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS one 10(8), e0133908 (2015).
[Crossref] [PubMed]

Song, Q.

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag. 32(2), 376–386 (2013).
[Crossref]

Q. Song, X. Wu, Y. Liu, M. Smith, J. Buatti, and M. Sonka, “Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2009), pp. 827–835.

Sonka, M.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
[Crossref]

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag. 32(2), 376–386 (2013).
[Crossref]

X. Liu, D. Z. Chen, M. H. Tawhai, X. Wu, E. A. Hoffman, and M. Sonka, “Optimal graph search based segmentation of airway tree double surfaces across bifurcations,” IEEE Trans. Med. Imag. 32(3), 493–510 (2013).
[Crossref]

S. Sun, M. Sonka, and R. R. Beichel, “Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface,” Computerized Medical Imaging and Graphics,  37(1), 15–27 (2013).
[Crossref] [PubMed]

Y. Yin, X. Zhang, R. Williams, X. Wu, D. D. Anderson, and M. Sonka, “Logismos layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint,” IEEE Trans. Med. Imag. 29(12), 2023–2037 (2010).
[Crossref]

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abrámoff, “Segmentation of the optic disc in 3-d oct scans of the optic nerve head,” IEEE Trans. Med. Imag. 29(1), 159–168 (2010).
[Crossref]

M. D. Abrámoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

M. K. Garvin, M. D. Abrámoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009).
[Crossref]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

A. Shah, J.-K. Wang, M. K. Garvin, M. Sonka, and X. Wu, “Automated surface segmentation of internal limiting membrane in spectral-domain optical coherence tomography volumes with a deep cup using a 3D range expansion approach,” in International Symposium on Biomedical Imaging (IEEE, 2014), pp. 1405–1408.

S. Kashyap, Y. Yin, and M. Sonka, “Automated analysis of cartilage morphology,” in International Symposium on Biomedical Imaging (IEEE, 2013), pp. 1300–1303.

Q. Song, X. Wu, Y. Liu, M. Smith, J. Buatti, and M. Sonka, “Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2009), pp. 827–835.

Sotirchos, E. S.

Stinson, W. G.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Sui, X.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
[Crossref]

Summers, R. M.

H. Greenspan, B. van Ginneken, and R. M. Summers, “Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,” IEEE Trans. Med. Imag. 35(5), 1153–1159 (2016).
[Crossref]

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition (IEEE, 2016), pp. 770–778.

Sun, S.

S. Sun, M. Sonka, and R. R. Beichel, “Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface,” Computerized Medical Imaging and Graphics,  37(1), 15–27 (2013).
[Crossref] [PubMed]

Sutskever, I.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), 1097–1105.

Swanson, E. A.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Szegedy, C.

S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” arXiv preprint 1502.03167, (2015).

Tatavarty, S.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

Tatrai, E.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. Somfai, W. Smiddy, and D. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophoton. 1(9), 478–489 (2016).
[Crossref]

Tavares, J. M. R.

Z. Ma, J. M. R. Tavares, and R. N. Jorge, “A review on the current segmentation algorithms for medical images,” in Proceedings of the 1st International Conference on Imaging Theory and Applications (2009).

Tawhai, M. H.

X. Liu, D. Z. Chen, M. H. Tawhai, X. Wu, E. A. Hoffman, and M. Sonka, “Optimal graph search based segmentation of airway tree double surfaces across bifurcations,” IEEE Trans. Med. Imag. 32(3), 493–510 (2013).
[Crossref]

Theelen, C T.

F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
[Crossref]

Tian, J.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. Somfai, W. Smiddy, and D. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophoton. 1(9), 478–489 (2016).
[Crossref]

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS one 10(8), e0133908 (2015).
[Crossref] [PubMed]

X. Zhang, J. Tian, K. Deng, Y. Wu, and X. Li, “Automatic liver segmentation using a statistical shape model with optimal surface detection,” IEEE Trans. Biomed. Eng. 57(10), 2622–2626 (2010).
[Crossref] [PubMed]

Toth, C. A.

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428(2015).
[Crossref]

S. Farsiu, S. J. Chiu, R. V. O Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmol. 121(1), pp. 162–172 (2014).
[Crossref]

van Ginneken, B.

H. Greenspan, B. van Ginneken, and R. M. Summers, “Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,” IEEE Trans. Med. Imag. 35(5), 1153–1159 (2016).
[Crossref]

VanderBeek, B. L.

M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in edi-oct images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis (Springer, 2017), pp. 177–184.
[Crossref]

Varga, B.

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. Somfai, W. Smiddy, and D. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophoton. 1(9), 478–489 (2016).
[Crossref]

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS one 10(8), e0133908 (2015).
[Crossref] [PubMed]

Venhuizen, F.

F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
[Crossref]

Vrtovec, T.

R. Korez, B. Likar, F. Pernus, and T. Vrtovec, “Model-based segmentation of vertebral bodies from MR images with 3D CNNs,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), pp. 433–441.

Wachinger, C.

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network,” arXiv preprint 1704.02161 (2017).

Wang, C.

Wang, J.

M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in edi-oct images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis (Springer, 2017), pp. 177–184.
[Crossref]

Wang, J.-K.

A. Shah, J.-K. Wang, M. K. Garvin, M. Sonka, and X. Wu, “Automated surface segmentation of internal limiting membrane in spectral-domain optical coherence tomography volumes with a deep cup using a 3D range expansion approach,” in International Symposium on Biomedical Imaging (IEEE, 2014), pp. 1405–1408.

Warde-Farley, D.

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

Wei, B.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
[Crossref]

Williams, R.

Y. Yin, X. Zhang, R. Williams, X. Wu, D. D. Anderson, and M. Sonka, “Logismos layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint,” IEEE Trans. Med. Imag. 29(12), 2023–2037 (2010).
[Crossref]

Withey, D. J.

D. J. Withey and Z. J. Koles, “A review of medical image segmentation: methods and available software,” International Journal of Bioelectromagnetism 10(3), 125–148 (2008).

Wolf-Schnurrbusch, U.

A. P. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Wu, J.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
[Crossref]

Wu, X.

X. Liu, D. Z. Chen, M. H. Tawhai, X. Wu, E. A. Hoffman, and M. Sonka, “Optimal graph search based segmentation of airway tree double surfaces across bifurcations,” IEEE Trans. Med. Imag. 32(3), 493–510 (2013).
[Crossref]

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag. 32(2), 376–386 (2013).
[Crossref]

Y. Yin, X. Zhang, R. Williams, X. Wu, D. D. Anderson, and M. Sonka, “Logismos layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint,” IEEE Trans. Med. Imag. 29(12), 2023–2037 (2010).
[Crossref]

M. K. Garvin, M. D. Abrámoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009).
[Crossref]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

A. Shah, J.-K. Wang, M. K. Garvin, M. Sonka, and X. Wu, “Automated surface segmentation of internal limiting membrane in spectral-domain optical coherence tomography volumes with a deep cup using a 3D range expansion approach,” in International Symposium on Biomedical Imaging (IEEE, 2014), pp. 1405–1408.

A. Shah, J. Bai, Z Hu, S. Sadda, and X. Wu, “Multiple Surface Segmentation Using Truncated Convex Priors,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 97–104.

A. Shah, M. D. Abramoff, and X. Wu, “Simultaneous multiple surface segmentation using deep learning,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 3–11.
[Crossref]

Q. Song, X. Wu, Y. Liu, M. Smith, J. Buatti, and M. Sonka, “Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2009), pp. 827–835.

Wu, Y.

X. Zhang, J. Tian, K. Deng, Y. Wu, and X. Li, “Automatic liver segmentation using a statistical shape model with optimal surface detection,” IEEE Trans. Biomed. Eng. 57(10), 2622–2626 (2010).
[Crossref] [PubMed]

Xiang, D.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
[Crossref]

Yazdanpanah, A.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intraretinal layer segmentation in optical coherence tomography using an active contour approach,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2009), pp. 649–656.

Yin, Y.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
[Crossref]

Y. Yin, X. Zhang, R. Williams, X. Wu, D. D. Anderson, and M. Sonka, “Logismos layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint,” IEEE Trans. Med. Imag. 29(12), 2023–2037 (2010).
[Crossref]

S. Kashyap, Y. Yin, and M. Sonka, “Automated analysis of cartilage morphology,” in International Symposium on Biomedical Imaging (IEEE, 2013), pp. 1300–1303.

Ying, H. S.

Yuan, E.

S. Farsiu, S. J. Chiu, R. V. O Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmol. 121(1), pp. 162–172 (2014).
[Crossref]

Zhang, S.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
[Crossref]

Zhang, X.

Y. Yin, X. Zhang, R. Williams, X. Wu, D. D. Anderson, and M. Sonka, “Logismos layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint,” IEEE Trans. Med. Imag. 29(12), 2023–2037 (2010).
[Crossref]

X. Zhang, J. Tian, K. Deng, Y. Wu, and X. Li, “Automatic liver segmentation using a statistical shape model with optimal surface detection,” IEEE Trans. Biomed. Eng. 57(10), 2622–2626 (2010).
[Crossref] [PubMed]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition (IEEE, 2016), pp. 770–778.

Zhao, H.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
[Crossref]

Zheng, Y.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
[Crossref]

Zhu, W.

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
[Crossref]

Biomed. Opt. Express (6)

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based CV model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
[Crossref] [PubMed]

L. de Sisternes, G. Jonna, J. Moss, M. F. Marmor, T. Leng, and D. L. Rubin, “Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes,” Biomed. Opt. Express 8(3), 1926–1949 (2017).
[Crossref] [PubMed]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
[Crossref] [PubMed]

B. J. Antony, M. D. Abrámoff, M. M. Harper, W. Jeong, E. H. Sohn, Y. H. Kwon, R. Kardon, and M. K. Garvin, “A combined machine learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes,” Biomed. Opt. Express 4(12), 2712–2728 (2013).
[Crossref]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref] [PubMed]

F. Venhuizen, B. Ginneken, B. Liefers, M. Grinsven, S. Fauser, C. Hoyng, C T. Theelen, and C. Sanchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 1(8), 3292–3316 (2017).
[Crossref]

Computerized Medical Imaging and Graphics (1)

S. Sun, M. Sonka, and R. R. Beichel, “Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface,” Computerized Medical Imaging and Graphics,  37(1), 15–27 (2013).
[Crossref] [PubMed]

IEEE Rev. Biomed. Eng. (1)

M. D. Abrámoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

IEEE Trans. Biomed. Eng. (2)

C. Bauer, M. A. Krueger, W. J. Lamm, B. J. Smith, R. W. Glenny, and R. R. Beichel, “Airway tree segmentation in serial block-face cryomicrotome images of rat lungs,” IEEE Trans. Biomed. Eng. 61(1), 119–130 (2014).
[Crossref]

X. Zhang, J. Tian, K. Deng, Y. Wu, and X. Li, “Automatic liver segmentation using a statistical shape model with optimal surface detection,” IEEE Trans. Biomed. Eng. 57(10), 2622–2626 (2010).
[Crossref] [PubMed]

IEEE Trans. Med. Imag. (7)

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abrámoff, “Segmentation of the optic disc in 3-d oct scans of the optic nerve head,” IEEE Trans. Med. Imag. 29(1), 159–168 (2010).
[Crossref]

H. Greenspan, B. van Ginneken, and R. M. Summers, “Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,” IEEE Trans. Med. Imag. 35(5), 1153–1159 (2016).
[Crossref]

Y. Yin, X. Zhang, R. Williams, X. Wu, D. D. Anderson, and M. Sonka, “Logismos layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint,” IEEE Trans. Med. Imag. 29(12), 2023–2037 (2010).
[Crossref]

X. Liu, D. Z. Chen, M. H. Tawhai, X. Wu, E. A. Hoffman, and M. Sonka, “Optimal graph search based segmentation of airway tree double surfaces across bifurcations,” IEEE Trans. Med. Imag. 32(3), 493–510 (2013).
[Crossref]

M. K. Garvin, M. D. Abrámoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag. 28(9), 1436–1447 (2009).
[Crossref]

F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, and H. Chen, “Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments,” IEEE Trans. Med. Imag. 34(2), 441–452 (2015).
[Crossref]

Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag. 32(2), 376–386 (2013).
[Crossref]

IEEE Trans. Med. Imaging (1)

A. P. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images-a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref]

Int. J. Comp. Vis. (1)

Y. Boykov and G. Funka-Lea, “Graph cuts and efficient and image segmentation,” Int. J. Comp. Vis. 70(2), 109–131 (2006).
[Crossref]

International Journal of Bioelectromagnetism (1)

D. J. Withey and Z. J. Koles, “A review of medical image segmentation: methods and available software,” International Journal of Bioelectromagnetism 10(3), 125–148 (2008).

J. Biophoton. (1)

J. Tian, B. Varga, E. Tatrai, P. Fanni, G. Somfai, W. Smiddy, and D. Debuc, “Performance evaluation of automated segmentation software on optical coherence tomography volume data,” J. Biophoton. 1(9), 478–489 (2016).
[Crossref]

J. Neurocomp. (1)

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” J. Neurocomp. 237, 332–341 (2017).
[Crossref]

JAMA (1)

N. M. Bressler, “Age-related macular degeneration is the leading cause of blindness,” JAMA 291(15), 1900–1901 (2004).
[Crossref] [PubMed]

Journal of Medical Signals and Sensors (1)

R. Kafieh, H. Rabbani, and S. Kermani, “A review of algorithms for segmentation of optical coherence tomography from retina,” Journal of Medical Signals and Sensors 3(1), 45 (2013).
[PubMed]

Medical Image Analysis (2)

K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical Image Analysis 36, 61–78 (2017).
[Crossref]

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis 35, 18–31 (2017).
[Crossref]

Nature (1)

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Ophthalmol. (1)

S. Farsiu, S. J. Chiu, R. V. O Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography,” Ophthalmol. 121(1), pp. 162–172 (2014).
[Crossref]

Opt. Express (1)

PloS one (1)

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS one 10(8), e0133908 (2015).
[Crossref] [PubMed]

Science (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, and C. A. Puliafito, “Optical coherence tomography,” Science 254(5035), 1178 (1991).
[Crossref] [PubMed]

Other (19)

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfier, M. Armbruster, F. Hofmann, and M. DAnastasi, “Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields,” in International Conference on Medical Image Computing and Computer Assisted Intervention (Springer, 2016), pp. 415–423.

R. Korez, B. Likar, F. Pernus, and T. Vrtovec, “Model-based segmentation of vertebral bodies from MR images with 3D CNNs,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), pp. 433–441.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition (IEEE, 2016), pp. 770–778.

S. Liao, Y. Gao, A. Oto, and D. Shen, “Representation learning: a unified deep learning framework for automatic prostate mr segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2013), pp. 254–261.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), 1097–1105.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 234–241.

A. Shah, J. Bai, Z Hu, S. Sadda, and X. Wu, “Multiple Surface Segmentation Using Truncated Convex Priors,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 97–104.

Z. Ma, J. M. R. Tavares, and R. N. Jorge, “A review on the current segmentation algorithms for medical images,” in Proceedings of the 1st International Conference on Imaging Theory and Applications (2009).

S. Kashyap, Y. Yin, and M. Sonka, “Automated analysis of cartilage morphology,” in International Symposium on Biomedical Imaging (IEEE, 2013), pp. 1300–1303.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intraretinal layer segmentation in optical coherence tomography using an active contour approach,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2009), pp. 649–656.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint 1408.5093 (2014).

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint 1412.6980, (2014).

Q. Song, X. Wu, Y. Liu, M. Smith, J. Buatti, and M. Sonka, “Optimal graph search segmentation using arc-weighted graph for simultaneous surface detection of bladder and prostate,” in Medical Image Computing and Computer-Assisted Intervention (Springer, 2009), pp. 827–835.

S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” arXiv preprint 1502.03167, (2015).

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network,” arXiv preprint 1704.02161 (2017).

O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer- Assisted Intervention, (Springer, 2016), pp. 424–432.

A. Shah, M. D. Abramoff, and X. Wu, “Simultaneous multiple surface segmentation using deep learning,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 3–11.
[Crossref]

M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in edi-oct images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis (Springer, 2017), pp. 177–184.
[Crossref]

A. Shah, J.-K. Wang, M. K. Garvin, M. Sonka, and X. Wu, “Automated surface segmentation of internal limiting membrane in spectral-domain optical coherence tomography volumes with a deep cup using a 3D range expansion approach,” in International Symposium on Biomedical Imaging (IEEE, 2014), pp. 1405–1408.

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Figures (6)

Fig. 1
Fig. 1 Example B-scan from an OCT image volume of (a) normal eye (b) eye with pathology (intermediate AMD). Yellow = Internal limiting membrane (ILM), red = Inner retinal pigment epithelium (IRPE) and green = outer aspect of the Bruch membrane (OBM). It can be seen that IRPE and OBM in the B-scan with intermediate AMD exhibits more changes in surface smoothness and surface distance between the two surfaces compared to the B-scan of normal eye.
Fig. 2
Fig. 2 Label vector encoding for a given image sample I with λ = 3 target surfaces. The indexes of vector positions are shown below each cell. The target label L(.) used in network training is the ordered concatenation of each surface encoded vector.
Fig. 3
Fig. 3 Network architecture for CNN-S with number of columns in B-scan = 400 and 3 target surfaces. N=number of kernels used in the convolution layer, CONV=Convolution Layer, FC=Fully connected layer, n=number of neurons in the FC layer.
Fig. 4
Fig. 4 Network architecture for UNET based methods. N=number of kernels used in a given layer, CONV=Convolution Layer, D-CONV=Deconvolution Layer, k=4 for UNET-1 and k=3 for UNET-2. Red arrows indicate concatenation operation.
Fig. 5
Fig. 5 The left column shows the same B-scan from a SD-OCT volume of a normal eye. The right column shows the same B-scan form a SD-OCT volume of an eye with intermediate AMD. Yellow = ILM, red = IRPE, green = OBM.
Fig. 6
Fig. 6 Each column shows the same B-scan from a SD-OCT volume of an eye with intermediate AMD. The right column illustrates an encountered failure case for the proposed CNN-S method. Yellow = ILM, red = IRPE, green = OBM.

Tables (3)

Tables Icon

Table 1 Unsigned mean surface positioning error (UMSP) (mean ± standard deviation) in μm. Obsv - Expert manual tracings.

Tables Icon

Table 2 Unsigned average symmetric surface distance error (UASSD) (mean ± standard deviation) in μm for normal case. Obsv - Expert manual tracings.

Tables Icon

Table 3 Unsigned average symmetric surface distance error (UASSD) (mean ± standard deviation) in μm for intermediate AMD case. Obsv - Expert manual tracings.

Equations (1)

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E = i = 1 λ x = 0 X 1 ( S i ( x ) L ( ( i 1 ) × X + x ) ) 2

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