Abstract

Active contours, or snakes, are widely applied on biomedical image segmentation. They are curves defined within an image domain that can move to object boundaries under the influence of internal forces and external forces, in which the internal forces are generally computed from curves themselves and external forces from image data. Designing external forces properly is a key point with active contour algorithms since the external forces play a leading role in the evolution of active contours. One of most popular external forces for active contour models is gradient vector flow (GVF). However, GVF is sensitive to noise and false edges, which limits its application area. To handle this problem, in this paper, we propose using GVF as reference to train aconvolutional neural network to derive an external force. The derived external force is then integrated into the active contour models for curve evolution. Three clinical applications, segmentation of optic disk in fundus images, fluid in retinal optical coherence tomography images and fetal head in ultrasound images, are employed to evaluate the proposed method. The results show that the proposed method is very promising since it achieves competitive performance for different tasks compared to the state-of-the-art algorithms.

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

Full Article  |  PDF Article
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2018 (6)

X. Chen and L. Pan, “A survey of graph cuts/graph search based medical image segmentation,” IEEE Rev. Biomed. Eng. 11, 112–124 (2018).
[Crossref] [PubMed]

T. H. N. Le, K. G. Quach, K. Luu, N. D. Chi, and M. Savvides, “Reformulating level sets as deep recurrent neural network approach to semantic segmentation,” IEEE Transactions on Image Process. 27, 2393–2407 (2018).
[Crossref]

T. L. A. V. D. Heuvel, D. D. Bruijn, C. L. D. Korte, and B. V. Ginneken, “Automated measurement of fetal head circumference using 2d ultrasound images,” Plos One 13, 1–20 (2018).

Z. Fan, Y. Rong, X. Cai, J. Lu, W. Li, H. Lin, and X. Chen, “Optic disk detection in fundus image based on structured learning,” IEEE J. Biomed. Heal. Informatics 22, 224–234 (2018).
[Crossref]

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic subretinal fluid segmentation of retinal sd-oct images with neurosensory retinal detachment guided by enface fundus imaging,” IEEE Transactions on Biomed. Eng. 65, 87–95 (2018).
[Crossref]

M. Liu, D. Cheng, K. Wang, and Y. Wang, “Multi-modality cascaded convolutional neural networks for alzheimer disease diagnosis,” Neuroinformatics 16, 1–14 (2018).
[Crossref]

2017 (3)

Y. Xu, K. Yan, J. Kim, X. Wang, C. Li, L. Su, S. Yu, X. Xu, and D. D. Feng, “Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy,” Biomed. Opt. Express 8, 4061–4076 (2017).
[Crossref] [PubMed]

A. Hoogi, A. Subramaniam, R. Veerapaneni, and D. L. Rubin, “Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis,” IEEE Transactions on Med. Imaging 36, 781–791 (2017).
[Crossref]

G. J. S. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. V. Der Laak, B. Van Ginneken, and C. I. Sanchez, “A survey on deep learning in medical image analysis,” Med. Image Analysis 42, 60–88 (2017).
[Crossref]

2016 (4)

S. Zhu and R. Gao, “A novel generalized gradient vector flow snake model using minimal surface and component-normalized method for medical image segmentation,” Biomed. Signal Process. & Control. 26, 1–10 (2016).
[Crossref]

M. R. Avendi, A. Kheradvar, and H. Jafarkhani, “A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri,” Med. Image Analysis 30, 108–119 (2016).
[Crossref]

T. Hassan, A. M. Usman, B. Hassan, A. M. Syed, and S. A. Bazaz, “Automated segmentation of subretinal layers for the detection of macular edema,” Appl. Opt. 55, 454–461 (2016).
[Crossref] [PubMed]

L. Zhang, X. Ye, T. Lambrou, W. Duan, N. Allinson, and N. J. Dudley, “A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2d ultrasound images,” Phys. Medicine & Biol. 61, 1095–1115 (2016).
[Crossref]

2015 (2)

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in oct,” Biomed. optics express 6, 155–169 (2015).
[Crossref]

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “Optic disc segmentation using the sliding band filter,” Comput. Biol. Medicine 56, 1–12 (2015).
[Crossref]

2014 (2)

S. Rueda, S. Fathima, C. L. Knight, M. Yaqub, A. T. Papageorghiou, B. Rahmatullah, A. Foi, M. Maggioni, A. Pepe, and J. Tohka, “Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge,” IEEE Transactions on Med. Imaging 33, 797 (2014).
[Crossref]

A. Giachetti, L. Ballerini, and E. Trucco, “Accurate and reliable segmentation of the optic disc in digital fundus images,” J. Med. Imaging 1, 1–11 (2014).
[Crossref]

2013 (2)

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Med. Imaging 32, 1019–1032 (2013).
[Crossref]

S. Morales, V. Naranjo, J. Angulo, and M. Alcañiz, “Automatic detection of optic disc based on pca and mathematical morphology,” IEEE transactions on Med. Imaging 32, 786–796 (2013).
[Crossref]

2012 (2)

B. Wu and Y. Yang, “Local- and global-statistics-based active contour model for image segmentation,” Math. Probl. Eng. 2012, 94–113 (2012).
[Crossref]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3d segmentation of fluid-associated abnormalities in retinal oct: Probability constrained graph-search -graph-cut,” IEEE transactions on Med. Imaging 31, 1521–1531 (2012).
[Crossref]

2010 (1)

A. Aquino, M. E. Gegúndez-Arias, and D. Marín, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” IEEE transactions on Med. Imaging 29, 1860–1869 (2010).
[Crossref]

2009 (1)

J. Tang, “A multi-direction gvf snake for the segmentation of skin cancer images,” Pattern Recognit. 42, 1172–1179 (2009).
[Crossref]

2008 (1)

E. J. Carmona, M. Rincón, J. García-Feijoó, and J. M. Martínez-de-la Casa, “Identification of the optic nerve head with genetic algorithms,” Artif. Intell. Medicine 43, 243–259 (2008).
[Crossref]

2007 (1)

L. Bing and S. T. Acton, “Active contour external force using vector field convolution for image segmentation,” IEEE Transactions on Image Process. 16, 2096–2106 (2007).
[Crossref]

2004 (2)

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Transactions on Med. Imaging 23, 256–264 (2004).
[Crossref]

D. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recognit. 37, 1–19 (2004).
[Crossref]

2001 (1)

S. D. Olabarriaga and A. W. M. Smeulders, “Interaction in the segmentation of medical images: A survey,” Med. Image Analysis 5, 127–142 (2001).
[Crossref]

1999 (1)

A. W. Fitzgibbon, M. Pilu, and R. B. Fisher, “Direct least squares fitting of ellipses,” IEEE Transactions on Pattern Analysis Mach. Intell. 21, 476–480 (1999).
[Crossref]

1998 (2)

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
[Crossref]

C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on Image Process. 7, 359–369 (1998).
[Crossref]

1988 (1)

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” Int. journal of computer vision 1, 321–331 (1988).
[Crossref]

1987 (1)

J. Illingworth and J. Kittle, “The adaptive hough transform,” IEEE Transactions on Pattern Analysis Mach. Intell. 9, 690–698 (1987).
[Crossref]

1975 (1)

N. Otsu, “A threshold selection method from gray-level histograms,” Automatica 11, 23–27 (1975).

Abràmoff, M. D.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3d segmentation of fluid-associated abnormalities in retinal oct: Probability constrained graph-search -graph-cut,” IEEE transactions on Med. Imaging 31, 1521–1531 (2012).
[Crossref]

Acton, S. T.

L. Bing and S. T. Acton, “Active contour external force using vector field convolution for image segmentation,” IEEE Transactions on Image Process. 16, 2096–2106 (2007).
[Crossref]

Alcañiz, M.

S. Morales, V. Naranjo, J. Angulo, and M. Alcañiz, “Automatic detection of optic disc based on pca and mathematical morphology,” IEEE transactions on Med. Imaging 32, 786–796 (2013).
[Crossref]

Allinson, N.

L. Zhang, X. Ye, T. Lambrou, W. Duan, N. Allinson, and N. J. Dudley, “A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2d ultrasound images,” Phys. Medicine & Biol. 61, 1095–1115 (2016).
[Crossref]

Al-Louzi, O.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in oct,” Biomed. optics express 6, 155–169 (2015).
[Crossref]

Angulo, J.

S. Morales, V. Naranjo, J. Angulo, and M. Alcañiz, “Automatic detection of optic disc based on pca and mathematical morphology,” IEEE transactions on Med. Imaging 32, 786–796 (2013).
[Crossref]

Aquino, A.

A. Aquino, M. E. Gegúndez-Arias, and D. Marín, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” IEEE transactions on Med. Imaging 29, 1860–1869 (2010).
[Crossref]

Araujo, L.

I. Zafar, G. Tzanidou, R. Burton, N. Patel, and L. Araujo, Hands-On Convolutional Neural Networks with TensorFlow (Packt Publishing Ltd, 201–202 (2018)).

Aung, T.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Med. Imaging 32, 1019–1032 (2013).
[Crossref]

Avendi, M. R.

M. R. Avendi, A. Kheradvar, and H. Jafarkhani, “A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri,” Med. Image Analysis 30, 108–119 (2016).
[Crossref]

Ballerini, L.

A. Giachetti, L. Ballerini, and E. Trucco, “Accurate and reliable segmentation of the optic disc in digital fundus images,” J. Med. Imaging 1, 1–11 (2014).
[Crossref]

Basu, A.

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Transactions on Med. Imaging 23, 256–264 (2004).
[Crossref]

Bazaz, S. A.

Bejnordi, B. E.

G. J. S. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. V. Der Laak, B. Van Ginneken, and C. I. Sanchez, “A survey on deep learning in medical image analysis,” Med. Image Analysis 42, 60–88 (2017).
[Crossref]

Bengio, Y.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
[Crossref]

Bhargava, P.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in oct,” Biomed. optics express 6, 155–169 (2015).
[Crossref]

Bing, L.

L. Bing and S. T. Acton, “Active contour external force using vector field convolution for image segmentation,” IEEE Transactions on Image Process. 16, 2096–2106 (2007).
[Crossref]

Bottou, L.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
[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, (2015), pp. 234–241.

Bruijn, D. D.

T. L. A. V. D. Heuvel, D. D. Bruijn, C. L. D. Korte, and B. V. Ginneken, “Automated measurement of fetal head circumference using 2d ultrasound images,” Plos One 13, 1–20 (2018).

Burton, R.

I. Zafar, G. Tzanidou, R. Burton, N. Patel, and L. Araujo, Hands-On Convolutional Neural Networks with TensorFlow (Packt Publishing Ltd, 201–202 (2018)).

Cai, X.

Z. Fan, Y. Rong, X. Cai, J. Lu, W. Li, H. Lin, and X. Chen, “Optic disk detection in fundus image based on structured learning,” IEEE J. Biomed. Heal. Informatics 22, 224–234 (2018).
[Crossref]

Calabresi, P. A.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in oct,” Biomed. optics express 6, 155–169 (2015).
[Crossref]

Campilho, A.

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “Optic disc segmentation using the sliding band filter,” Comput. Biol. Medicine 56, 1–12 (2015).
[Crossref]

Carass, A.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in oct,” Biomed. optics express 6, 155–169 (2015).
[Crossref]

Carmona, E. J.

E. J. Carmona, M. Rincón, J. García-Feijoó, and J. M. Martínez-de-la Casa, “Identification of the optic nerve head with genetic algorithms,” Artif. Intell. Medicine 43, 243–259 (2008).
[Crossref]

Chen, Q.

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic subretinal fluid segmentation of retinal sd-oct images with neurosensory retinal detachment guided by enface fundus imaging,” IEEE Transactions on Biomed. Eng. 65, 87–95 (2018).
[Crossref]

Z. Ji, Q. Chen, M. Wu, S. Niu, W. Fan, S. Yuan, and Q. Sun, “Beyond retinal layers: A large blob detection for subretinal fluid segmentation in sd-oct images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2018), pp. 372–380.

Chen, X.

X. Chen and L. Pan, “A survey of graph cuts/graph search based medical image segmentation,” IEEE Rev. Biomed. Eng. 11, 112–124 (2018).
[Crossref] [PubMed]

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G. Lim, Y. Cheng, W. Hsu, and M. L. Lee, “Integrated optic disc and cup segmentation with deep learning,” in IEEE International Conference on TOOLS with Artificial Intelligence, (2016), pp. 162–169.

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T. H. N. Le, K. G. Quach, K. Luu, N. D. Chi, and M. Savvides, “Reformulating level sets as deep recurrent neural network approach to semantic segmentation,” IEEE Transactions on Image Process. 27, 2393–2407 (2018).
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T. L. A. V. D. Heuvel, D. D. Bruijn, C. L. D. Korte, and B. V. Ginneken, “Automated measurement of fetal head circumference using 2d ultrasound images,” Plos One 13, 1–20 (2018).

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L. Zhang, X. Ye, T. Lambrou, W. Duan, N. Allinson, and N. J. Dudley, “A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2d ultrasound images,” Phys. Medicine & Biol. 61, 1095–1115 (2016).
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Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
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X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3d segmentation of fluid-associated abnormalities in retinal oct: Probability constrained graph-search -graph-cut,” IEEE transactions on Med. Imaging 31, 1521–1531 (2012).
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Setio, A. A. A.

G. J. S. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. V. Der Laak, B. Van Ginneken, and C. I. Sanchez, “A survey on deep learning in medical image analysis,” Med. Image Analysis 42, 60–88 (2017).
[Crossref]

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S. D. Olabarriaga and A. W. M. Smeulders, “Interaction in the segmentation of medical images: A survey,” Med. Image Analysis 5, 127–142 (2001).
[Crossref]

Sofka, M.

F. Milletari, A. Rothberg, J. Jia, and M. Sofka, “Integrating statistical prior knowledge into convolutional neural networks,” in International Conference on Medical Image Computing & Computer-assisted Intervention, (2017), pp. 161–168.

Sonka, M.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3d segmentation of fluid-associated abnormalities in retinal oct: Probability constrained graph-search -graph-cut,” IEEE transactions on Med. Imaging 31, 1521–1531 (2012).
[Crossref]

Srivastava, R.

R. Srivastava, J. Cheng, D. W. K. Wong, and J. Liu, “Using deep learning for robustness to parapapillary atrophy in optic disc segmentation,” in IEEE International Symposium on Biomedical Imaging, (2015), pp. 768–771.

Steel, D.

J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” IEEE Transactions on Med. Imaging 23, 256–264 (2004).
[Crossref]

Su, L.

Subramaniam, A.

A. Hoogi, A. Subramaniam, R. Veerapaneni, and D. L. Rubin, “Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis,” IEEE Transactions on Med. Imaging 36, 781–791 (2017).
[Crossref]

Sun, Q.

Z. Ji, Q. Chen, M. Wu, S. Niu, W. Fan, S. Yuan, and Q. Sun, “Beyond retinal layers: A large blob detection for subretinal fluid segmentation in sd-oct images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2018), pp. 372–380.

Swingle, E. K.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in oct,” Biomed. optics express 6, 155–169 (2015).
[Crossref]

Syed, A. M.

Tan, N.-M.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Med. Imaging 32, 1019–1032 (2013).
[Crossref]

Tang, J.

J. Tang, “A multi-direction gvf snake for the segmentation of skin cancer images,” Pattern Recognit. 42, 1172–1179 (2009).
[Crossref]

Tao, D.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Med. Imaging 32, 1019–1032 (2013).
[Crossref]

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M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” Int. journal of computer vision 1, 321–331 (1988).
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S. Rueda, S. Fathima, C. L. Knight, M. Yaqub, A. T. Papageorghiou, B. Rahmatullah, A. Foi, M. Maggioni, A. Pepe, and J. Tohka, “Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge,” IEEE Transactions on Med. Imaging 33, 797 (2014).
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A. Giachetti, L. Ballerini, and E. Trucco, “Accurate and reliable segmentation of the optic disc in digital fundus images,” J. Med. Imaging 1, 1–11 (2014).
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I. Zafar, G. Tzanidou, R. Burton, N. Patel, and L. Araujo, Hands-On Convolutional Neural Networks with TensorFlow (Packt Publishing Ltd, 201–202 (2018)).

Usman, A. M.

Van Ginneken, B.

G. J. S. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. V. Der Laak, B. Van Ginneken, and C. I. Sanchez, “A survey on deep learning in medical image analysis,” Med. Image Analysis 42, 60–88 (2017).
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A. Vedaldi and K. Lenc, “Matconvnet – convolutional neural networks for matlab,” in Proceeding of the ACM Int. Conf. on Multimedia, (2015), pp. 689–692.

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A. Hoogi, A. Subramaniam, R. Veerapaneni, and D. L. Rubin, “Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis,” IEEE Transactions on Med. Imaging 36, 781–791 (2017).
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M. Liu, D. Cheng, K. Wang, and Y. Wang, “Multi-modality cascaded convolutional neural networks for alzheimer disease diagnosis,” Neuroinformatics 16, 1–14 (2018).
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L. Wu, Y. Xin, S. Li, T. Wang, P. A. Heng, and D. Ni, “Cascaded fully convolutional networks for automatic prenatal ultrasound image segmentation,” in IEEE International Symposium on Biomedical Imaging, (2017), pp. 663–666.

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M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” Int. journal of computer vision 1, 321–331 (1988).
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J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Med. Imaging 32, 1019–1032 (2013).
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J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Med. Imaging 32, 1019–1032 (2013).
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B. Wu and Y. Yang, “Local- and global-statistics-based active contour model for image segmentation,” Math. Probl. Eng. 2012, 94–113 (2012).
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Z. Li, A. Lin, X. Yang, and J. Wu, “Left ventricle segmentation by combining convolution neural network with active contour model and tensor voting in short-axis mri,” in IEEE International Conference on Bioinformatics and Biomedicine, (2017), pp. 736–739.

Wu, L.

L. Wu, Y. Xin, S. Li, T. Wang, P. A. Heng, and D. Ni, “Cascaded fully convolutional networks for automatic prenatal ultrasound image segmentation,” in IEEE International Symposium on Biomedical Imaging, (2017), pp. 663–666.

Wu, M.

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic subretinal fluid segmentation of retinal sd-oct images with neurosensory retinal detachment guided by enface fundus imaging,” IEEE Transactions on Biomed. Eng. 65, 87–95 (2018).
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Z. Ji, Q. Chen, M. Wu, S. Niu, W. Fan, S. Yuan, and Q. Sun, “Beyond retinal layers: A large blob detection for subretinal fluid segmentation in sd-oct images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2018), pp. 372–380.

Xin, Y.

L. Wu, Y. Xin, S. Li, T. Wang, P. A. Heng, and D. Ni, “Cascaded fully convolutional networks for automatic prenatal ultrasound image segmentation,” in IEEE International Symposium on Biomedical Imaging, (2017), pp. 663–666.

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Yan, K.

Yang, X.

Z. Li, A. Lin, X. Yang, and J. Wu, “Left ventricle segmentation by combining convolution neural network with active contour model and tensor voting in short-axis mri,” in IEEE International Conference on Bioinformatics and Biomedicine, (2017), pp. 736–739.

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B. Wu and Y. Yang, “Local- and global-statistics-based active contour model for image segmentation,” Math. Probl. Eng. 2012, 94–113 (2012).
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S. Rueda, S. Fathima, C. L. Knight, M. Yaqub, A. T. Papageorghiou, B. Rahmatullah, A. Foi, M. Maggioni, A. Pepe, and J. Tohka, “Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge,” IEEE Transactions on Med. Imaging 33, 797 (2014).
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Ye, X.

L. Zhang, X. Ye, T. Lambrou, W. Duan, N. Allinson, and N. J. Dudley, “A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2d ultrasound images,” Phys. Medicine & Biol. 61, 1095–1115 (2016).
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Yin, F.

J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel classification based optic disc and optic cup segmentation for glaucoma screening,” IEEE transactions on Med. Imaging 32, 1019–1032 (2013).
[Crossref]

Ying, H. S.

A. Lang, A. Carass, E. K. Swingle, O. Al-Louzi, P. Bhargava, S. Saidha, H. S. Ying, P. A. Calabresi, and J. L. Prince, “Automatic segmentation of microcystic macular edema in oct,” Biomed. optics express 6, 155–169 (2015).
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Yu, S.

Yuan, S.

M. Wu, Q. Chen, X. He, P. Li, W. Fan, S. Yuan, and H. Park, “Automatic subretinal fluid segmentation of retinal sd-oct images with neurosensory retinal detachment guided by enface fundus imaging,” IEEE Transactions on Biomed. Eng. 65, 87–95 (2018).
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Z. Ji, Q. Chen, M. Wu, S. Niu, W. Fan, S. Yuan, and Q. Sun, “Beyond retinal layers: A large blob detection for subretinal fluid segmentation in sd-oct images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2018), pp. 372–380.

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I. Zafar, G. Tzanidou, R. Burton, N. Patel, and L. Araujo, Hands-On Convolutional Neural Networks with TensorFlow (Packt Publishing Ltd, 201–202 (2018)).

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D. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recognit. 37, 1–19 (2004).
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X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3d segmentation of fluid-associated abnormalities in retinal oct: Probability constrained graph-search -graph-cut,” IEEE transactions on Med. Imaging 31, 1521–1531 (2012).
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R. Srivastava, J. Cheng, D. W. K. Wong, and J. Liu, “Using deep learning for robustness to parapapillary atrophy in optic disc segmentation,” in IEEE International Symposium on Biomedical Imaging, (2015), pp. 768–771.

L. Wu, Y. Xin, S. Li, T. Wang, P. A. Heng, and D. Ni, “Cascaded fully convolutional networks for automatic prenatal ultrasound image segmentation,” in IEEE International Symposium on Biomedical Imaging, (2017), pp. 663–666.

Z. Ji, Q. Chen, M. Wu, S. Niu, W. Fan, S. Yuan, and Q. Sun, “Beyond retinal layers: A large blob detection for subretinal fluid segmentation in sd-oct images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (2018), pp. 372–380.

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Figures (8)

Fig. 1
Fig. 1 An example to demonstrate different external forces obtained by different ways. (a) The GVF derived from the gradient of original image. (b) The detected result (blue curve) with (a). (c) The external force derived by the trained CNN. (d) The detected result (blue curve) with (c).
Fig. 2
Fig. 2 An example to demonstrate the process of obtaining the referenced GVF. (a) Raw image. (b) Boundary ground truth of (a). (c) Referenced GVF.
Fig. 3
Fig. 3 The structure of GVF-Net.
Fig. 4
Fig. 4 An example to demonstrate the process of automatic initialization of curve. (a) The external force. (b) The magnitude of (a). (c) Maximum connected region of (b). (d) The initial curve (red) and final result (blue).
Fig. 5
Fig. 5 An example to show the interactive initialization. (a) The external force magnitude and initial curve. (b) The fetal heal boundary obtained by active contour model (red curve) and the result of ellipse fitting (blue curve).
Fig. 6
Fig. 6 Examples for optic disk segmentation(first row), fetal head segmentation (second row) and fluid segmentation (third row), where green curves are ground truth and blue curves are curves detected by the proposed algorithm.
Fig. 7
Fig. 7 An example to explain why the proposed method might fail or succeed. (a) Unsatisfactory external force. (b) Initial curve (red), detected result (blue) with (a) and ground truth (green). (c) Satisfactory external force. (d) Initial curve (red), detected result (blue) with (c) and ground truth (green).
Fig. 8
Fig. 8 Scatterplot of region area between manual segmentation and automatic segmentation.

Tables (8)

Tables Icon

Table 1 The average values and standard deviations of different performance metrics achieved on different databases.

Tables Icon

Table 2 The performance of the proposed method for fetal head segmentation.

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Table 3 The percentage of images with AOL > 0.8 in different databases.

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Table 4 Average AOL and DSC obtained by different methods.

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Table 5 Comparison with different methods for optic disk segmentation with AOL metric.

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Table 6 Comparison with different methods for fluid segmentation with DSC metric.

Tables Icon

Table 7 Comparison with different methods for fetal head circumference measurement.

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Table 8 Comparison with and without fine tuning on DRIONS and ONHSD databases.

Equations (15)

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

E = 0 1 1 2 [ α | x ( s ) | 2 + β | x ( s ) | 2 ] + E e x t ( x ( s ) ) d s
α x ( s ) β x ( s ) E e x t = 0
F i n t + F e x t = 0
ε = μ ( u x 2 + u y 2 + v x 2 + v y 2 ) + | f | 2 | v f | 2 d x d y
μ 2 u ( u f x ) ( f x 2 + f y 2 ) = 0 μ 2 v ( v f y ) ( f x 2 + f y 2 ) = 0
α x ( s ) β x ( s ) ϕ ( I , w ) = 0
w ^ = arg  min w i = 1 N | v i ϕ ( I i , w ) | 2
A O L = T P / ( T P + F N + F P )
D S C = 2 T P / ( 2 T P + F N + F P )
A c = ( T P + T N ) / ( T P + T N + F P + F N )
T P F = T P / ( T P + F N )
F P F = F P / ( F P + T N )
D F = H C S H C R
A D F = | H C S H C R |
H D = max  ( h ( S , R ) , h ( R , S ) )