Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images

Pengshuai Yin; Mingkui Tan; Huaqing Min; Yanwu Xu; Guanghui Xu; Qingyao Wu; Yunfei Tong; Higashita Risa; Jiang Liu

doi:10.1007/978-3-030-00949-6_32

Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images

Pengshuai Yin, Mingkui Tan, Huaqing Min, Yanwu Xu, Guanghui Xu, Qingyao Wu, Yunfei Tong, Higashita Risa, Jiang Liu

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

We propose a pipeline for automatically segmenting cortex and nucleus in a 360-degree anterior segment optical coherence tomography (AS-OCT) image. The proposed pipeline consists of a U-shaped network followed by a shape template. The U-shaped network predicts a mask for cortex and nucleus. However, the boundary between cortex and nucleus is weak, so that the boundary of the prediction is an irregular shape and does not satisfy the physiological structure of nucleus. To address this problem, in the second step, we design a shape template according to the physiological structure of nucleus to refine the boundary. Our method integrates both appearance and structure information. The accuracy is measured by the normalized mean squared error (NMSE) between ground truth line and predicted line. We achieve NMSE 7.09/7.94 for nucleus top/bottom boundary and 2.49/2.43 for cortex top/bottom boundary.

Original language	English
Title of host publication	Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings
Editors	Zeike Taylor, Hrvoje Bogunovic, David Snead, Mona K. Garvin, Xin Jan Chen, Francesco Ciompi, Yanwu Xu, Lena Maier-Hein, Mitko Veta, Emanuele Trucco, Danail Stoyanov, Nasir Rajpoot, Jeroen van der Laak, Anne Martel, Stephen McKenna
Publisher	Springer Verlag
Pages	269-276
Number of pages	8
ISBN (Print)	9783030009489
DOIs	https://doi.org/10.1007/978-3-030-00949-6_32
Publication status	Published - 2018
Externally published	Yes
Event	1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018 - Granada, Spain Duration: 16 Sept 2018 → 20 Sept 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11039 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16/09/18 → 20/09/18

Keywords

AS-OCT
Image segmentation
SS-OCT

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-00949-6_32

Cite this

Yin, P., Tan, M., Min, H., Xu, Y., Xu, G., Wu, Q., Tong, Y., Risa, H., & Liu, J. (2018). Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images. In Z. Taylor, H. Bogunovic, D. Snead, M. K. Garvin, X. J. Chen, F. Ciompi, Y. Xu, L. Maier-Hein, M. Veta, E. Trucco, D. Stoyanov, N. Rajpoot, J. van der Laak, A. Martel, & S. McKenna (Eds.), Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 269-276). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11039 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00949-6_32

Yin, Pengshuai ; Tan, Mingkui ; Min, Huaqing et al. / Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images. Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. editor / Zeike Taylor ; Hrvoje Bogunovic ; David Snead ; Mona K. Garvin ; Xin Jan Chen ; Francesco Ciompi ; Yanwu Xu ; Lena Maier-Hein ; Mitko Veta ; Emanuele Trucco ; Danail Stoyanov ; Nasir Rajpoot ; Jeroen van der Laak ; Anne Martel ; Stephen McKenna. Springer Verlag, 2018. pp. 269-276 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images",

abstract = "We propose a pipeline for automatically segmenting cortex and nucleus in a 360-degree anterior segment optical coherence tomography (AS-OCT) image. The proposed pipeline consists of a U-shaped network followed by a shape template. The U-shaped network predicts a mask for cortex and nucleus. However, the boundary between cortex and nucleus is weak, so that the boundary of the prediction is an irregular shape and does not satisfy the physiological structure of nucleus. To address this problem, in the second step, we design a shape template according to the physiological structure of nucleus to refine the boundary. Our method integrates both appearance and structure information. The accuracy is measured by the normalized mean squared error (NMSE) between ground truth line and predicted line. We achieve NMSE 7.09/7.94 for nucleus top/bottom boundary and 2.49/2.43 for cortex top/bottom boundary.",

keywords = "AS-OCT, Image segmentation, SS-OCT",

author = "Pengshuai Yin and Mingkui Tan and Huaqing Min and Yanwu Xu and Guanghui Xu and Qingyao Wu and Yunfei Tong and Higashita Risa and Jiang Liu",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018 ; Conference date: 16-09-2018 Through 20-09-2018",

year = "2018",

doi = "10.1007/978-3-030-00949-6_32",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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editor = "Zeike Taylor and Hrvoje Bogunovic and David Snead and Garvin, {Mona K.} and Chen, {Xin Jan} and Francesco Ciompi and Yanwu Xu and Lena Maier-Hein and Mitko Veta and Emanuele Trucco and Danail Stoyanov and Nasir Rajpoot and {van der Laak}, Jeroen and Anne Martel and Stephen McKenna",

booktitle = "Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings",

address = "Germany",

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Yin, P, Tan, M, Min, H, Xu, Y, Xu, G, Wu, Q, Tong, Y, Risa, H & Liu, J 2018, Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images. in Z Taylor, H Bogunovic, D Snead, MK Garvin, XJ Chen, F Ciompi, Y Xu, L Maier-Hein, M Veta, E Trucco, D Stoyanov, N Rajpoot, J van der Laak, A Martel & S McKenna (eds), Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11039 LNCS, Springer Verlag, pp. 269-276, 1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-00949-6_32

Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images. / Yin, Pengshuai; Tan, Mingkui; Min, Huaqing et al.
Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. ed. / Zeike Taylor; Hrvoje Bogunovic; David Snead; Mona K. Garvin; Xin Jan Chen; Francesco Ciompi; Yanwu Xu; Lena Maier-Hein; Mitko Veta; Emanuele Trucco; Danail Stoyanov; Nasir Rajpoot; Jeroen van der Laak; Anne Martel; Stephen McKenna. Springer Verlag, 2018. p. 269-276 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11039 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images

AU - Yin, Pengshuai

AU - Tan, Mingkui

AU - Min, Huaqing

AU - Xu, Yanwu

AU - Xu, Guanghui

AU - Wu, Qingyao

AU - Tong, Yunfei

AU - Risa, Higashita

AU - Liu, Jiang

PY - 2018

Y1 - 2018

N2 - We propose a pipeline for automatically segmenting cortex and nucleus in a 360-degree anterior segment optical coherence tomography (AS-OCT) image. The proposed pipeline consists of a U-shaped network followed by a shape template. The U-shaped network predicts a mask for cortex and nucleus. However, the boundary between cortex and nucleus is weak, so that the boundary of the prediction is an irregular shape and does not satisfy the physiological structure of nucleus. To address this problem, in the second step, we design a shape template according to the physiological structure of nucleus to refine the boundary. Our method integrates both appearance and structure information. The accuracy is measured by the normalized mean squared error (NMSE) between ground truth line and predicted line. We achieve NMSE 7.09/7.94 for nucleus top/bottom boundary and 2.49/2.43 for cortex top/bottom boundary.

AB - We propose a pipeline for automatically segmenting cortex and nucleus in a 360-degree anterior segment optical coherence tomography (AS-OCT) image. The proposed pipeline consists of a U-shaped network followed by a shape template. The U-shaped network predicts a mask for cortex and nucleus. However, the boundary between cortex and nucleus is weak, so that the boundary of the prediction is an irregular shape and does not satisfy the physiological structure of nucleus. To address this problem, in the second step, we design a shape template according to the physiological structure of nucleus to refine the boundary. Our method integrates both appearance and structure information. The accuracy is measured by the normalized mean squared error (NMSE) between ground truth line and predicted line. We achieve NMSE 7.09/7.94 for nucleus top/bottom boundary and 2.49/2.43 for cortex top/bottom boundary.

KW - AS-OCT

KW - Image segmentation

KW - SS-OCT

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DO - 10.1007/978-3-030-00949-6_32

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SN - 9783030009489

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings

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A2 - Bogunovic, Hrvoje

A2 - Snead, David

A2 - Garvin, Mona K.

A2 - Chen, Xin Jan

A2 - Ciompi, Francesco

A2 - Xu, Yanwu

A2 - Maier-Hein, Lena

A2 - Veta, Mitko

A2 - Trucco, Emanuele

A2 - Stoyanov, Danail

A2 - Rajpoot, Nasir

A2 - van der Laak, Jeroen

A2 - Martel, Anne

A2 - McKenna, Stephen

PB - Springer Verlag

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Y2 - 16 September 2018 through 20 September 2018

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Yin P, Tan M, Min H, Xu Y, Xu G, Wu Q et al. Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images. In Taylor Z, Bogunovic H, Snead D, Garvin MK, Chen XJ, Ciompi F, Xu Y, Maier-Hein L, Veta M, Trucco E, Stoyanov D, Rajpoot N, van der Laak J, Martel A, McKenna S, editors, Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer Verlag. 2018. p. 269-276. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00949-6_32