Abstract

Optical coherence tomography (OCT) enables the non-invasive acquisition of high-resolution three-dimensional cross-sectional images at micrometer scale and is mainly used in the field of ophthalmology for diagnosis as well as monitoring of eye diseases. Also in other areas, such as dermatology, OCT is already well established. Due to its non-invasive nature, OCT is also employed for research studies involving animal models. Manual evaluation of OCT images of animal models is a challenging task due to the lack of imaging standards and the varying anatomy between models. In this paper, we present a deep learning algorithm for the automatic segmentation of several layers of mouse skin in OCT image data using a deep convolutional neural network (CNN). The architecture of our CNN is based on the U-net and is modified by densely-connected convolutions. We compared our adapted CNN with our previous algorithm, a combination of a random forest classification and a graph-based refinement, and a baseline U-net. The results showed that, on average, our proposed CNN outperformed our previous algorithm and the baseline U-net. In addition, a reduction of outliers could be observed through the use of densely-connected convolutions.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
OSA Recommended Articles
Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks

Freerk G. Venhuizen, Bram van Ginneken, Bart Liefers, Mark J.J.P. van Grinsven, Sascha Fauser, Carel Hoyng, Thomas Theelen, and Clara I. Sánchez
Biomed. Opt. Express 8(7) 3292-3316 (2017)

Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images

Abhay Shah, Leixin Zhou, Michael D. Abrámoff, and Xiaodong Wu
Biomed. Opt. Express 9(9) 4509-4526 (2018)

Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2

Jessica Loo, Leyuan Fang, David Cunefare, Glenn J. Jaffe, and Sina Farsiu
Biomed. Opt. Express 9(6) 2681-2698 (2018)

References

  • View by:
  • |
  • |
  • |

  1. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
    [Crossref] [PubMed]
  2. M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
    [Crossref] [PubMed]
  3. J. Welzel, “Optical coherence tomography in dermatology: a review,” Ski. Res. Technol. Rev. article 7, 1–9 (2001).
    [Crossref]
  4. T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40, 85–94 (2005).
    [Crossref] [PubMed]
  5. A. J. Singer, Z. Wang, S. A. McClain, and Y. Pan, “Optical coherence tomography: a noninvasive method to assess wound reepithelialization,” Acad. Emerg. Medicine 14, 387–391 (2007).
    [Crossref]
  6. S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
    [Crossref] [PubMed]
  7. N. Salma, M. Evers, M. J. Casper, C. Droigk, T. Kepp, H. Handels, and D. Manstein, “Mouse model of cold-induced localized fat loss (selective cryolipolysis),” in Lasers in Surgery and Medicine, vol. 50 (Wiley, 2018), pp. S19–S20.
  8. 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, 119–134 (2006).
    [Crossref]
  9. M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
    [Crossref]
  10. 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, 19413–19428 (2010).
    [Crossref] [PubMed]
  11. R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3d optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
    [Crossref]
  12. S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6, 1172–1194 (2015).
    [Crossref] [PubMed]
  13. 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 networks,” Biomed. Opt. Express 8, 3627–3642 (2017).
    [Crossref] [PubMed]
  14. F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8, 3292–3316 (2017).
    [Crossref] [PubMed]
  15. S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological oct retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2017), pp. 294–301.
  16. S. K. Devalla, P. K. Renukanand, B. K. Sreedhar, G. Subramanian, L. Zhang, S. Perera, J.-M. Mari, K. S. Chin, T. A. Tun, N. G. Strouthidis, and et al., “DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images,” Biomed. Opt. Express 9, 3244–3265 (2018).
    [Crossref] [PubMed]
  17. B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.
  18. 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, 2732–2744 (2017).
    [Crossref] [PubMed]
  19. 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.
  20. A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30, 484–496 (2011).
    [Crossref]
  21. A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17, 23719–23728 (2009).
    [Crossref]
  22. K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–576 (2003).
    [Crossref] [PubMed]
  23. P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
    [Crossref] [PubMed]
  24. D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
    [Crossref]
  25. D. Sheet, S. P. K. Karri, A. Katouzian, N. Navab, A. K. Ray, and J. Chatterjee, “Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology,” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), (IEEE, 2015), pp. 777–780.
    [Crossref]
  26. T. Kepp, C. Droigk, M. Casper, M. Evers, N. Salma, D. Manstein, and H. Handels, “Segmentation of subcutaneous fat within mouse skin in 3d OCT image data using random forests,” in Medical Imaging 2018: Image Processing, vol. 10574 (SPIE, 2018), pp. 1057426–1–1057426–8.
  27. J. Rogowska and M. E. Brezinski, “Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images,” Phys. Med. Biol. 47, 641 (2002).
    [Crossref]
  28. D. Manstein, H. Laubach, K. Watanabe, W. Farinelli, D. Zurakowski, and R. R. Anderson, “Selective cryolysis: A novel method of non-invasive fat removal,” Lasers Surg. Medicine 40, 595–604 (2008).
    [Crossref]
  29. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE2017), pp. 4700–4708.
  30. P. A. Yushkevich, J. Piven, H. Cody Hazlett, R. Gimpel Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 1116–1128 (2006).
    [Crossref] [PubMed]
  31. A. Odena, V. Dumoulin, and C. Olah, “Deconvolution and checkerboard artifacts,” Distill 1, e3 (2016).
    [Crossref]
  32. S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” https://arxiv.org/abs/1502.03167.
  33. 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.
  34. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” https://arxiv.org/abs/1412.6980.
  35. F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV) (IEEE, 2016), pp. 565–571.
    [Crossref]
  36. C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso, “Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 240–248.
    [Crossref]
  37. X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS, 2010), pp. 249–256.
  38. A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

2018 (1)

2017 (3)

2016 (1)

A. Odena, V. Dumoulin, and C. Olah, “Deconvolution and checkerboard artifacts,” Distill 1, e3 (2016).
[Crossref]

2015 (1)

2014 (1)

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

2013 (2)

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3d optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
[Crossref]

2011 (1)

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30, 484–496 (2011).
[Crossref]

2010 (1)

2009 (1)

2008 (2)

D. Manstein, H. Laubach, K. Watanabe, W. Farinelli, D. Zurakowski, and R. R. Anderson, “Selective cryolysis: A novel method of non-invasive fat removal,” Lasers Surg. Medicine 40, 595–604 (2008).
[Crossref]

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

2007 (1)

A. J. Singer, Z. Wang, S. A. McClain, and Y. Pan, “Optical coherence tomography: a noninvasive method to assess wound reepithelialization,” Acad. Emerg. Medicine 14, 387–391 (2007).
[Crossref]

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, 119–134 (2006).
[Crossref]

P. A. Yushkevich, J. Piven, H. Cody Hazlett, R. Gimpel Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 1116–1128 (2006).
[Crossref] [PubMed]

2005 (1)

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40, 85–94 (2005).
[Crossref] [PubMed]

2004 (1)

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

2003 (1)

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–576 (2003).
[Crossref] [PubMed]

2002 (1)

J. Rogowska and M. E. Brezinski, “Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images,” Phys. Med. Biol. 47, 641 (2002).
[Crossref]

2001 (1)

J. Welzel, “Optical coherence tomography in dermatology: a review,” Ski. Res. Technol. Rev. article 7, 1–9 (2001).
[Crossref]

1995 (1)

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[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, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Abramoff, M. D.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3d optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
[Crossref]

Abràmoff, M. D.

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

Ahmadi, S.-A.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV) (IEEE, 2016), pp. 565–571.
[Crossref]

Allingham, M. J.

Al-Louzi, O.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.

Altmeyer, P.

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40, 85–94 (2005).
[Crossref] [PubMed]

Anderson, R. R.

D. Manstein, H. Laubach, K. Watanabe, W. Farinelli, D. Zurakowski, and R. R. Anderson, “Selective cryolysis: A novel method of non-invasive fat removal,” Lasers Surg. Medicine 40, 595–604 (2008).
[Crossref]

Antiga, L.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

Antony, B. J.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.

Apostolopoulos, S.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological oct retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2017), pp. 294–301.

Applegate, B. E.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Banerjee, P.

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

Barton, J. K.

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–576 (2003).
[Crossref] [PubMed]

Bengio, Y.

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS, 2010), pp. 249–256.

Bizheva, K.

Brandon, J. L.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Brezinski, M. E.

J. Rogowska and M. E. Brezinski, “Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images,” Phys. Med. Biol. 47, 641 (2002).
[Crossref]

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.

Calabresi, P. A.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.

Carass, A.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.

Cardoso, M. J.

C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso, “Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 240–248.
[Crossref]

Casper, M.

T. Kepp, C. Droigk, M. Casper, M. Evers, N. Salma, D. Manstein, and H. Handels, “Segmentation of subcutaneous fat within mouse skin in 3d OCT image data using random forests,” in Medical Imaging 2018: Image Processing, vol. 10574 (SPIE, 2018), pp. 1057426–1–1057426–8.

Casper, M. J.

N. Salma, M. Evers, M. J. Casper, C. Droigk, T. Kepp, H. Handels, and D. Manstein, “Mouse model of cold-induced localized fat loss (selective cryolipolysis),” in Lasers in Surgery and Medicine, vol. 50 (Wiley, 2018), pp. S19–S20.

Chanan, G.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

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, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Chatterjee, J.

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

D. Sheet, S. P. K. Karri, A. Katouzian, N. Navab, A. K. Ray, and J. Chatterjee, “Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology,” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), (IEEE, 2015), pp. 777–780.
[Crossref]

Chaudhary, A.

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

Chen, D. Z.

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, 119–134 (2006).
[Crossref]

Chen, Z.

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

Cheng, Y.-S.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Chin, K. S.

Chintala, S.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

Chiu, S. J.

Ciller, C.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological oct retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2017), pp. 294–301.

Clausi, D. A.

Cody Hazlett, H.

P. A. Yushkevich, J. Piven, H. Cody Hazlett, R. Gimpel Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 1116–1128 (2006).
[Crossref] [PubMed]

Conjeti, S.

Cousins, S. W.

Cunefare, D.

Das, D.

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

de Boer, J. F.

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

De Zanet, S.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological oct retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2017), pp. 294–301.

Desmaison, A.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

Devalla, S. K.

DeVito, Z.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

Droigk, C.

T. Kepp, C. Droigk, M. Casper, M. Evers, N. Salma, D. Manstein, and H. Handels, “Segmentation of subcutaneous fat within mouse skin in 3d OCT image data using random forests,” in Medical Imaging 2018: Image Processing, vol. 10574 (SPIE, 2018), pp. 1057426–1–1057426–8.

N. Salma, M. Evers, M. J. Casper, C. Droigk, T. Kepp, H. Handels, and D. Manstein, “Mouse model of cold-induced localized fat loss (selective cryolipolysis),” in Lasers in Surgery and Medicine, vol. 50 (Wiley, 2018), pp. S19–S20.

Dumoulin, V.

A. Odena, V. Dumoulin, and C. Olah, “Deconvolution and checkerboard artifacts,” Distill 1, e3 (2016).
[Crossref]

Evers, M.

T. Kepp, C. Droigk, M. Casper, M. Evers, N. Salma, D. Manstein, and H. Handels, “Segmentation of subcutaneous fat within mouse skin in 3d OCT image data using random forests,” in Medical Imaging 2018: Image Processing, vol. 10574 (SPIE, 2018), pp. 1057426–1–1057426–8.

N. Salma, M. Evers, M. J. Casper, C. Droigk, T. Kepp, H. Handels, and D. Manstein, “Mouse model of cold-induced localized fat loss (selective cryolipolysis),” in Lasers in Surgery and Medicine, vol. 50 (Wiley, 2018), pp. S19–S20.

Fang, L.

Farinelli, W.

D. Manstein, H. Laubach, K. Watanabe, W. Farinelli, D. Zurakowski, and R. R. Anderson, “Selective cryolysis: A novel method of non-invasive fat removal,” Lasers Surg. Medicine 40, 595–604 (2008).
[Crossref]

Farsiu, S.

Fauser, S.

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, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Fujimoto, J. G.

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[Crossref] [PubMed]

Gambichler, T.

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40, 85–94 (2005).
[Crossref] [PubMed]

Garvin, M. K.

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

Gee, J. C.

P. A. Yushkevich, J. Piven, H. Cody Hazlett, R. Gimpel Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 1116–1128 (2006).
[Crossref] [PubMed]

Gerig, G.

P. A. Yushkevich, J. Piven, H. Cody Hazlett, R. Gimpel Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 1116–1128 (2006).
[Crossref] [PubMed]

Gimenez-Conti, I.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Gimpel Smith, R.

P. A. Yushkevich, J. Piven, H. Cody Hazlett, R. Gimpel Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 1116–1128 (2006).
[Crossref] [PubMed]

Glorot, X.

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS, 2010), pp. 249–256.

Gossage, K. W.

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–576 (2003).
[Crossref] [PubMed]

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, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Gross, S.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

Guymer, R. H.

Hamarneh, G.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30, 484–496 (2011).
[Crossref]

Handels, H.

T. Kepp, C. Droigk, M. Casper, M. Evers, N. Salma, D. Manstein, and H. Handels, “Segmentation of subcutaneous fat within mouse skin in 3d OCT image data using random forests,” in Medical Imaging 2018: Image Processing, vol. 10574 (SPIE, 2018), pp. 1057426–1–1057426–8.

N. Salma, M. Evers, M. J. Casper, C. Droigk, T. Kepp, H. Handels, and D. Manstein, “Mouse model of cold-induced localized fat loss (selective cryolipolysis),” in Lasers in Surgery and Medicine, vol. 50 (Wiley, 2018), pp. S19–S20.

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.

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Ho, S.

P. A. Yushkevich, J. Piven, H. Cody Hazlett, R. Gimpel Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 1116–1128 (2006).
[Crossref] [PubMed]

Hoffmann, K.

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40, 85–94 (2005).
[Crossref] [PubMed]

Hoyng, C.

Huang, D.

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Huang, G.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE2017), pp. 4700–4708.

Huang, H.-E. L.

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

Izatt, J. A.

Jo, J. A.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Jung, W. G.

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

Kafieh, R.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3d optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
[Crossref]

Kardon, R.

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

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 networks,” Biomed. Opt. Express 8, 3627–3642 (2017).
[Crossref] [PubMed]

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

D. Sheet, S. P. K. Karri, A. Katouzian, N. Navab, A. K. Ray, and J. Chatterjee, “Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology,” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), (IEEE, 2015), pp. 777–780.
[Crossref]

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 networks,” Biomed. Opt. Express 8, 3627–3642 (2017).
[Crossref] [PubMed]

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

D. Sheet, S. P. K. Karri, A. Katouzian, N. Navab, A. K. Ray, and J. Chatterjee, “Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology,” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), (IEEE, 2015), pp. 777–780.
[Crossref]

Keikhanzadeh, K.

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

Kepp, T.

N. Salma, M. Evers, M. J. Casper, C. Droigk, T. Kepp, H. Handels, and D. Manstein, “Mouse model of cold-induced localized fat loss (selective cryolipolysis),” in Lasers in Surgery and Medicine, vol. 50 (Wiley, 2018), pp. S19–S20.

T. Kepp, C. Droigk, M. Casper, M. Evers, N. Salma, D. Manstein, and H. Handels, “Segmentation of subcutaneous fat within mouse skin in 3d OCT image data using random forests,” in Medical Imaging 2018: Image Processing, vol. 10574 (SPIE, 2018), pp. 1057426–1–1057426–8.

Lang, A.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.

Laubach, H.

D. Manstein, H. Laubach, K. Watanabe, W. Farinelli, D. Zurakowski, and R. R. Anderson, “Selective cryolysis: A novel method of non-invasive fat removal,” Lasers Surg. Medicine 40, 595–604 (2008).
[Crossref]

Lerer, A.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

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, 119–134 (2006).
[Crossref]

Li, S.

Li, W.

C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso, “Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 240–248.
[Crossref]

Li, X. T.

Liefers, B.

Lin, C. P.

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Lin, Z.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

Liu, Z.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE2017), pp. 4700–4708.

Manstein, D.

D. Manstein, H. Laubach, K. Watanabe, W. Farinelli, D. Zurakowski, and R. R. Anderson, “Selective cryolysis: A novel method of non-invasive fat removal,” Lasers Surg. Medicine 40, 595–604 (2008).
[Crossref]

T. Kepp, C. Droigk, M. Casper, M. Evers, N. Salma, D. Manstein, and H. Handels, “Segmentation of subcutaneous fat within mouse skin in 3d OCT image data using random forests,” in Medical Imaging 2018: Image Processing, vol. 10574 (SPIE, 2018), pp. 1057426–1–1057426–8.

N. Salma, M. Evers, M. J. Casper, C. Droigk, T. Kepp, H. Handels, and D. Manstein, “Mouse model of cold-induced localized fat loss (selective cryolipolysis),” in Lasers in Surgery and Medicine, vol. 50 (Wiley, 2018), pp. S19–S20.

Mari, J.-M.

McClain, S. A.

A. J. Singer, Z. Wang, S. A. McClain, and Y. Pan, “Optical coherence tomography: a noninvasive method to assess wound reepithelialization,” Acad. Emerg. Medicine 14, 387–391 (2007).
[Crossref]

Mettu, P. S.

Milletari, F.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV) (IEEE, 2016), pp. 565–571.
[Crossref]

Mishra, A.

Moussa, G.

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40, 85–94 (2005).
[Crossref] [PubMed]

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 networks,” Biomed. Opt. Express 8, 3627–3642 (2017).
[Crossref] [PubMed]

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

D. Sheet, S. P. K. Karri, A. Katouzian, N. Navab, A. K. Ray, and J. Chatterjee, “Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology,” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), (IEEE, 2015), pp. 777–780.
[Crossref]

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV) (IEEE, 2016), pp. 565–571.
[Crossref]

Nelson, J. S.

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

Nicholas, P.

Odena, A.

A. Odena, V. Dumoulin, and C. Olah, “Deconvolution and checkerboard artifacts,” Distill 1, e3 (2016).
[Crossref]

Olah, C.

A. Odena, V. Dumoulin, and C. Olah, “Deconvolution and checkerboard artifacts,” Distill 1, e3 (2016).
[Crossref]

Ourselin, S.

C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso, “Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 240–248.
[Crossref]

Pan, Y.

A. J. Singer, Z. Wang, S. A. McClain, and Y. Pan, “Optical coherence tomography: a noninvasive method to assess wound reepithelialization,” Acad. Emerg. Medicine 14, 387–391 (2007).
[Crossref]

Pande, P.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Park, B. H.

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

Park, J.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Paszke, A.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

Perera, S.

Piven, J.

P. A. Yushkevich, J. Piven, H. Cody Hazlett, R. Gimpel Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 1116–1128 (2006).
[Crossref] [PubMed]

Prince, J. L.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.

Puliafito, C. A.

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Rabbani, H.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3d optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
[Crossref]

Ray, A. K.

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

D. Sheet, S. P. K. Karri, A. Katouzian, N. Navab, A. K. Ray, and J. Chatterjee, “Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology,” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), (IEEE, 2015), pp. 777–780.
[Crossref]

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.

Renukanand, P. K.

Rodriguez, J. J.

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–576 (2003).
[Crossref] [PubMed]

Rogowska, J.

J. Rogowska and M. E. Brezinski, “Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images,” Phys. Med. Biol. 47, 641 (2002).
[Crossref]

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.

Roy, A. G.

Russell, S. R.

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

Saidha, S.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.

Salma, N.

N. Salma, M. Evers, M. J. Casper, C. Droigk, T. Kepp, H. Handels, and D. Manstein, “Mouse model of cold-induced localized fat loss (selective cryolipolysis),” in Lasers in Surgery and Medicine, vol. 50 (Wiley, 2018), pp. S19–S20.

T. Kepp, C. Droigk, M. Casper, M. Evers, N. Salma, D. Manstein, and H. Handels, “Segmentation of subcutaneous fat within mouse skin in 3d OCT image data using random forests,” in Medical Imaging 2018: Image Processing, vol. 10574 (SPIE, 2018), pp. 1057426–1–1057426–8.

Sánchez, C. I.

Sand, D.

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40, 85–94 (2005).
[Crossref] [PubMed]

Sand, M.

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40, 85–94 (2005).
[Crossref] [PubMed]

Sarunic, M. V.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30, 484–496 (2011).
[Crossref]

Schuman, J. S.

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Serafino, M. J.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

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 networks,” Biomed. Opt. Express 8, 3627–3642 (2017).
[Crossref] [PubMed]

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

D. Sheet, S. P. K. Karri, A. Katouzian, N. Navab, A. K. Ray, and J. Chatterjee, “Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology,” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), (IEEE, 2015), pp. 777–780.
[Crossref]

Shrestha, S.

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

Singer, A. J.

A. J. Singer, Z. Wang, S. A. McClain, and Y. Pan, “Optical coherence tomography: a noninvasive method to assess wound reepithelialization,” Acad. Emerg. Medicine 14, 387–391 (2007).
[Crossref]

Smith, B. R.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30, 484–496 (2011).
[Crossref]

Solomon, S.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.

Sonka, M.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3d optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
[Crossref]

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[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, 119–134 (2006).
[Crossref]

Sreedhar, B. K.

Srinivas, S. M.

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

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, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Strouthidis, N. G.

Subramanian, G.

Sudre, C. H.

C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso, “Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 240–248.
[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.

Swanson, E. A.

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Swingle, E. K.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.

Sznitman, R.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological oct retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2017), pp. 294–301.

Theelen, T.

Tkaczyk, T. S.

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–576 (2003).
[Crossref] [PubMed]

Toth, C. A.

Tun, T. A.

Van Der Maaten, L.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE2017), pp. 4700–4708.

van Ginneken, B.

van Grinsven, M. J.

Venhuizen, F. G.

Vercauteren, T.

C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso, “Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 240–248.
[Crossref]

Wachinger, C.

Wang, C.

Wang, Z.

A. J. Singer, Z. Wang, S. A. McClain, and Y. Pan, “Optical coherence tomography: a noninvasive method to assess wound reepithelialization,” Acad. Emerg. Medicine 14, 387–391 (2007).
[Crossref]

Watanabe, K.

D. Manstein, H. Laubach, K. Watanabe, W. Farinelli, D. Zurakowski, and R. R. Anderson, “Selective cryolysis: A novel method of non-invasive fat removal,” Lasers Surg. Medicine 40, 595–604 (2008).
[Crossref]

Weinberger, K. Q.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE2017), pp. 4700–4708.

Welzel, J.

J. Welzel, “Optical coherence tomography in dermatology: a review,” Ski. Res. Technol. Rev. article 7, 1–9 (2001).
[Crossref]

Wolf, S.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological oct retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2017), pp. 294–301.

Wong, A.

Wu, X.

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[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, 119–134 (2006).
[Crossref]

Yang, E.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

Yazdanpanah, A.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30, 484–496 (2011).
[Crossref]

Yushkevich, P. A.

P. A. Yushkevich, J. Piven, H. Cody Hazlett, R. Gimpel Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 1116–1128 (2006).
[Crossref] [PubMed]

Zhang, J.

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

Zhang, L.

Zhang, X.

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.

Zurakowski, D.

D. Manstein, H. Laubach, K. Watanabe, W. Farinelli, D. Zurakowski, and R. R. Anderson, “Selective cryolysis: A novel method of non-invasive fat removal,” Lasers Surg. Medicine 40, 595–604 (2008).
[Crossref]

Acad. Emerg. Medicine (1)

A. J. Singer, Z. Wang, S. A. McClain, and Y. Pan, “Optical coherence tomography: a noninvasive method to assess wound reepithelialization,” Acad. Emerg. Medicine 14, 387–391 (2007).
[Crossref]

Arch. Ophthalmol. (1)

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113, 325–332 (1995).
[Crossref] [PubMed]

Biomed. Opt. Express (5)

Distill (1)

A. Odena, V. Dumoulin, and C. Olah, “Deconvolution and checkerboard artifacts,” Distill 1, e3 (2016).
[Crossref]

IEEE Trans. Med. Imaging (2)

M. K. Garvin, M. D. Abràmoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref]

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30, 484–496 (2011).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (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, 119–134 (2006).
[Crossref]

J. Biomed. Opt. (4)

S. M. Srinivas, J. F. de Boer, B. H. Park, K. Keikhanzadeh, H.-E. L. Huang, J. Zhang, W. G. Jung, Z. Chen, and J. S. Nelson, “Determination of burn depth by polarization-sensitive optical coherence tomography,” J. Biomed. Opt. 9, 207–213 (2004).
[Crossref] [PubMed]

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–576 (2003).
[Crossref] [PubMed]

P. Pande, S. Shrestha, J. Park, M. J. Serafino, I. Gimenez-Conti, J. L. Brandon, Y.-S. Cheng, B. E. Applegate, and J. A. Jo, “Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch,” J. Biomed. Opt. 19, 086022 (2014).
[Crossref] [PubMed]

D. Sheet, A. Chaudhary, S. P. K. Karri, D. Das, A. Katouzian, P. Banerjee, N. Navab, J. Chatterjee, and A. K. Ray, “In situ histology of mice skin through transfer learning of tissue energy interaction in optical coherence tomography,” J. Biomed. Opt. 18, 090503 (2013).
[Crossref]

J. Dermatol. Sci. (1)

T. Gambichler, G. Moussa, M. Sand, D. Sand, P. Altmeyer, and K. Hoffmann, “Applications of optical coherence tomography in dermatology,” J. Dermatol. Sci. 40, 85–94 (2005).
[Crossref] [PubMed]

Lasers Surg. Medicine (1)

D. Manstein, H. Laubach, K. Watanabe, W. Farinelli, D. Zurakowski, and R. R. Anderson, “Selective cryolysis: A novel method of non-invasive fat removal,” Lasers Surg. Medicine 40, 595–604 (2008).
[Crossref]

Med. Image Anal. (1)

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3d optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17, 907–928 (2013).
[Crossref]

Neuroimage (1)

P. A. Yushkevich, J. Piven, H. Cody Hazlett, R. Gimpel Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” Neuroimage 31, 1116–1128 (2006).
[Crossref] [PubMed]

Opt. Express (2)

Phys. Med. Biol. (1)

J. Rogowska and M. E. Brezinski, “Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images,” Phys. Med. Biol. 47, 641 (2002).
[Crossref]

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, C. A. Puliafito, and et al., “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

Ski. Res. Technol. Rev. article (1)

J. Welzel, “Optical coherence tomography in dermatology: a review,” Ski. Res. Technol. Rev. article 7, 1–9 (2001).
[Crossref]

Other (14)

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological oct retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2017), pp. 294–301.

B. J. Antony, A. Lang, E. K. Swingle, O. Al-Louzi, A. Carass, S. Solomon, P. A. Calabresi, S. Saidha, and J. L. Prince, “Simultaneous segmentation of retinal surfaces and microcystic macular edema in SDOCT volumes,” in Medical Imaging 2016: Image Processing, vol. 9784 (International Society for Optics and Photonics, 2016), p. 97841C.

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.

N. Salma, M. Evers, M. J. Casper, C. Droigk, T. Kepp, H. Handels, and D. Manstein, “Mouse model of cold-induced localized fat loss (selective cryolipolysis),” in Lasers in Surgery and Medicine, vol. 50 (Wiley, 2018), pp. S19–S20.

D. Sheet, S. P. K. Karri, A. Katouzian, N. Navab, A. K. Ray, and J. Chatterjee, “Deep learning of tissue specific speckle representations in optical coherence tomography and deeper exploration for in situ histology,” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), (IEEE, 2015), pp. 777–780.
[Crossref]

T. Kepp, C. Droigk, M. Casper, M. Evers, N. Salma, D. Manstein, and H. Handels, “Segmentation of subcutaneous fat within mouse skin in 3d OCT image data using random forests,” in Medical Imaging 2018: Image Processing, vol. 10574 (SPIE, 2018), pp. 1057426–1–1057426–8.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE2017), pp. 4700–4708.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” https://arxiv.org/abs/1502.03167.

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.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” https://arxiv.org/abs/1412.6980.

F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV) (IEEE, 2016), pp. 565–571.
[Crossref]

C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso, “Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 240–248.
[Crossref]

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS, 2010), pp. 249–256.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in 31st Conference on Neural Information Processing Systems (NIPS, 2017), pp. 1–4.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (7)

Fig. 1
Fig. 1 Flowcharts of our previous approach [26] a), where we used a random forest classifier in combination with graph-based refinement, and our new CNN-based method b).
Fig. 2
Fig. 2 OCT scan of mouse skin of the inguinal area. (a) B-scan of inguinal mouse skin. Note that the sample spacer is visible as a bright horizontal line in the upper part of the image, whereby it is difficult to distinguish between epidermis and dermis at contact points. The red rectangle in the overview image (b) visualizes the field of view and the dashed line indicates the scan position. A histological section of mouse skin is given in (c).
Fig. 3
Fig. 3 Architecture of our proposed DCU-net.
Fig. 4
Fig. 4 Segmentation results of a single OCT B-scan. The B-scan without segmentation overlay is shown in a)/b), with expert annotations in c)/d) and with segmentations by RF+GC, U-net and DCU-net in e)/f), g)/h) and i)/j), respectively. e), g), j) or f), i), k) show segmentation results of the 18FCV which was performed with annotations of expert 1/2. Color mapping is explained as follows: DL boe----i001.gif, SFL boe----i002.gif, FML boe----i003.gif and tattoos boe----i004.gif. Note that the FML was not segmented by the RF+GC algorithm.
Fig. 5
Fig. 5 Segmentation results of a single OCT B-scan. The B-scan without segmentation overlay is shown in a)/b), with expert annotations in c)/d) and with segmentations by RF+GC, U-net and DCU-net in e)/f), g)/h) and i)/j), respectively. e), g), j) or f), i), k) show segmentation results of the 18FCV which was performed with annotations of expert 1/2. Color mapping is explained as follows: DL boe----i001.gif, SFL boe----i002.gif, FML boe----i003.gif and tattoos boe----i004.gif. Note that the FML was not segmented by the RF+GC algorithm.
Fig. 6
Fig. 6 Quantitative analysis of the comparative algorithms. Box plots represent the result distributions of the averaged metrics for DL, SFL and FML from both 18FCVs. Note that the FML was not calculated by the RF+GC algorithm.
Fig. 7
Fig. 7 Thickness map visualization of the SFL. Each single B-scan of the OCT volume was segmented by the DCU-net. Remaining outliers were eliminated using minor postprocessing steps.

Tables (1)

Tables Icon

Table 1 Average results of the two 18FCVs are presented, conducted with annotations of the respective expert. The best performances are printed in bold. The distances for ASSD and HD are given in μm (pixel height and width correspond to 6.8 μm and 13 μm, respectively). Note that the FML class was not computed by the RF+GC algorithm. In addition, the inter-rater reliability (IRR) between both experts was determined.

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

𝒥 DSC = 2 l α l x Ω p l ( x ) g l ( x ) + ε l α l x Ω p l 2 ( x ) + x Ω g l 2 ( x ) + ε
SD ( S P , S G ) = i = 0 n 2 min 0 j < n 1 p j q i 2 .
ASSD = SD ( S P , S G ) 2 n 2 + SD ( S G , S P ) 2 n 1 .
HD = max [ SD ( S P , S G ) , SD ( S G , S P ) ] .

Metrics