Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning
Purpose The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise segmentation of the optic cup (OC) and optic disc (OD). In recent years, convolution neural networks (CNN) have shown outstanding performance in medic...
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Published in | International journal for computer assisted radiology and surgery Vol. 16; no. 6; pp. 905 - 914 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Cham
Springer International Publishing
01.06.2021
Springer Nature B.V |
Subjects | |
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Abstract | Purpose
The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise segmentation of the optic cup (OC) and optic disc (OD). In recent years, convolution neural networks (CNN) have shown outstanding performance in medical segmentation tasks. However, most CNN-based methods ignore the effect of boundary ambiguity on performance, which leads to low generalization. This paper is dedicated to solving this issue.
Methods
In this paper, we propose a novel segmentation architecture, called BGA-Net, which introduces an auxiliary boundary branch and adversarial learning to jointly segment OD and OC in a multi-label manner. To generate more accurate results, the generative adversarial network is exploited to encourage boundary and mask predictions to be similar to the ground truth ones.
Results
Experimental results show that our BGA-Net system achieves state-of-the-art OC and OD segmentation performance on three publicly available datasets, i.e., the Dice scores for the optic disc/cup on the Drishti-GS, RIM-ONE-r3 and REFUGE datasets are 0.975/0.898, 0.967/0.872 and 0.951/0.866, respectively.
Conclusion
In this work, we not only achieve superior OD and OC segmentation results, but also confirm that the values calculated through the geometric relationship between the former two are highly related to glaucoma. |
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AbstractList | PurposeThe most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise segmentation of the optic cup (OC) and optic disc (OD). In recent years, convolution neural networks (CNN) have shown outstanding performance in medical segmentation tasks. However, most CNN-based methods ignore the effect of boundary ambiguity on performance, which leads to low generalization. This paper is dedicated to solving this issue.MethodsIn this paper, we propose a novel segmentation architecture, called BGA-Net, which introduces an auxiliary boundary branch and adversarial learning to jointly segment OD and OC in a multi-label manner. To generate more accurate results, the generative adversarial network is exploited to encourage boundary and mask predictions to be similar to the ground truth ones.ResultsExperimental results show that our BGA-Net system achieves state-of-the-art OC and OD segmentation performance on three publicly available datasets, i.e., the Dice scores for the optic disc/cup on the Drishti-GS, RIM-ONE-r3 and REFUGE datasets are 0.975/0.898, 0.967/0.872 and 0.951/0.866, respectively.ConclusionIn this work, we not only achieve superior OD and OC segmentation results, but also confirm that the values calculated through the geometric relationship between the former two are highly related to glaucoma. Purpose The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise segmentation of the optic cup (OC) and optic disc (OD). In recent years, convolution neural networks (CNN) have shown outstanding performance in medical segmentation tasks. However, most CNN-based methods ignore the effect of boundary ambiguity on performance, which leads to low generalization. This paper is dedicated to solving this issue. Methods In this paper, we propose a novel segmentation architecture, called BGA-Net, which introduces an auxiliary boundary branch and adversarial learning to jointly segment OD and OC in a multi-label manner. To generate more accurate results, the generative adversarial network is exploited to encourage boundary and mask predictions to be similar to the ground truth ones. Results Experimental results show that our BGA-Net system achieves state-of-the-art OC and OD segmentation performance on three publicly available datasets, i.e., the Dice scores for the optic disc/cup on the Drishti-GS, RIM-ONE-r3 and REFUGE datasets are 0.975/0.898, 0.967/0.872 and 0.951/0.866, respectively. Conclusion In this work, we not only achieve superior OD and OC segmentation results, but also confirm that the values calculated through the geometric relationship between the former two are highly related to glaucoma. The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise segmentation of the optic cup (OC) and optic disc (OD). In recent years, convolution neural networks (CNN) have shown outstanding performance in medical segmentation tasks. However, most CNN-based methods ignore the effect of boundary ambiguity on performance, which leads to low generalization. This paper is dedicated to solving this issue. In this paper, we propose a novel segmentation architecture, called BGA-Net, which introduces an auxiliary boundary branch and adversarial learning to jointly segment OD and OC in a multi-label manner. To generate more accurate results, the generative adversarial network is exploited to encourage boundary and mask predictions to be similar to the ground truth ones. Experimental results show that our BGA-Net system achieves state-of-the-art OC and OD segmentation performance on three publicly available datasets, i.e., the Dice scores for the optic disc/cup on the Drishti-GS, RIM-ONE-r3 and REFUGE datasets are 0.975/0.898, 0.967/0.872 and 0.951/0.866, respectively. In this work, we not only achieve superior OD and OC segmentation results, but also confirm that the values calculated through the geometric relationship between the former two are highly related to glaucoma. |
Author | Pan, Feng Luo, Ling Xue, Dingyu Feng, Xinglong |
Author_xml | – sequence: 1 givenname: Ling surname: Luo fullname: Luo, Ling organization: Northeastern University – sequence: 2 givenname: Dingyu surname: Xue fullname: Xue, Dingyu email: xuedingyu@mail.neu.edu.cn organization: Northeastern University – sequence: 3 givenname: Feng surname: Pan fullname: Pan, Feng organization: Northeastern University – sequence: 4 givenname: Xinglong surname: Feng fullname: Feng, Xinglong organization: Northeastern University |
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Cites_doi | 10.1016/j.bspc.2019.101832 10.1109/TMI.2019.2899910 10.1136/bjo.82.4.352 10.1109/TBME.2019.2913211 10.1109/TMI.2003.823261 10.1109/JBHI.2019.2899403 10.1109/TMI.2011.2106509 10.1007/s10278-018-0126-3 10.1109/TMI.2018.2791488 10.1001/jamaophthalmol.2015.1110 10.1016/j.compmedimag.2019.02.005 10.1016/j.media.2019.101570 10.1109/TMI.2013.2247770 10.1016/j.neucom.2019.05.039 10.1007/s13246-015-0377-y 10.1109/ISBI.2014.6867807 10.1109/IEMBS.2010.5626137 10.1109/CVPR.2017.195 10.1109/CVPR.2018.00566 10.1007/978-3-030-01234-2_49 10.1109/3DV.2016.79 10.1109/CVPR.2018.00474 10.1109/CBMS.2011.5999143 10.1007/978-3-030-32239-7_49 10.1007/978-3-642-40763-5_10 10.1007/978-3-030-32239-7_12 |
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The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise... The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise segmentation of... PurposeThe most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise... PURPOSEThe most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise... |
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SubjectTerms | Artificial neural networks Computer Imaging Computer Science Convolution Datasets Generative adversarial networks Glaucoma Health Informatics Imaging Learning Medicine Medicine & Public Health Original Article Pattern Recognition and Graphics Radiology Segmentation Surgery Vision |
Title | Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning |
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