Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net
Diabetic retinopathy (DR) is a prevalent vision-threatening disease worldwide. Laser marks are the scars left after panretinal photocoagulation, a treatment to prevent patients with severe DR from losing vision. In this study, we develop a deep learning algorithm based on the lightweight U-Net to se...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2021/8766517 |
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| author | Yukang Jiang Jianying Pan Ming Yuan Yanhe Shen Jin Zhu Yishen Wang Yewei Li Ke Zhang Qingyun Yu Huirui Xie Huiting Li Xueqin Wang Yan Luo |
| author_facet | Yukang Jiang Jianying Pan Ming Yuan Yanhe Shen Jin Zhu Yishen Wang Yewei Li Ke Zhang Qingyun Yu Huirui Xie Huiting Li Xueqin Wang Yan Luo |
| author_sort | Yukang Jiang |
| collection | DOAJ |
| description | Diabetic retinopathy (DR) is a prevalent vision-threatening disease worldwide. Laser marks are the scars left after panretinal photocoagulation, a treatment to prevent patients with severe DR from losing vision. In this study, we develop a deep learning algorithm based on the lightweight U-Net to segment laser marks from the color fundus photos, which could help indicate a stage or providing valuable auxiliary information for the care of DR patients. We prepared our training and testing data, manually annotated by trained and experienced graders from Image Reading Center, Zhongshan Ophthalmic Center, publicly available to fill the vacancy of public image datasets dedicated to the segmentation of laser marks. The lightweight U-Net, along with two postprocessing procedures, achieved an AUC of 0.9824, an optimal sensitivity of 94.16%, and an optimal specificity of 92.82% on the segmentation of laser marks in fundus photographs. With accurate segmentation and high numeric metrics, the lightweight U-Net method showed its reliable performance in automatically segmenting laser marks in fundus photographs, which could help the AI assist the diagnosis of DR in the severe stage. |
| format | Article |
| id | doaj-art-33219738020a4a8599ac2b24f754c56d |
| institution | Kabale University |
| issn | 2314-6745 2314-6753 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-33219738020a4a8599ac2b24f754c56d2025-02-03T01:25:08ZengWileyJournal of Diabetes Research2314-67452314-67532021-01-01202110.1155/2021/87665178766517Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-NetYukang Jiang0Jianying Pan1Ming Yuan2Yanhe Shen3Jin Zhu4Yishen Wang5Yewei Li6Ke Zhang7Qingyun Yu8Huirui Xie9Huiting Li10Xueqin Wang11Yan Luo12State Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, ChinaState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, ChinaDepartment of Statistical Science, School of Mathematics, Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou 510275, ChinaDepartment of Statistical Science, School of Mathematics, Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou 510275, ChinaDepartment of Statistical Science, School of Mathematics, Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou 510275, ChinaState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, ChinaDepartment of Statistical Science, School of Mathematics, Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou 510275, ChinaDepartment of Statistical Science, School of Mathematics, Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou 510275, ChinaDepartment of Statistical Science, School of Mathematics, Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou 510275, ChinaState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, ChinaState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, ChinaDepartment of Statistical Science, School of Mathematics, Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou 510275, ChinaState Key Laboratory of Ophthalmology, Image Reading Center, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, ChinaDiabetic retinopathy (DR) is a prevalent vision-threatening disease worldwide. Laser marks are the scars left after panretinal photocoagulation, a treatment to prevent patients with severe DR from losing vision. In this study, we develop a deep learning algorithm based on the lightweight U-Net to segment laser marks from the color fundus photos, which could help indicate a stage or providing valuable auxiliary information for the care of DR patients. We prepared our training and testing data, manually annotated by trained and experienced graders from Image Reading Center, Zhongshan Ophthalmic Center, publicly available to fill the vacancy of public image datasets dedicated to the segmentation of laser marks. The lightweight U-Net, along with two postprocessing procedures, achieved an AUC of 0.9824, an optimal sensitivity of 94.16%, and an optimal specificity of 92.82% on the segmentation of laser marks in fundus photographs. With accurate segmentation and high numeric metrics, the lightweight U-Net method showed its reliable performance in automatically segmenting laser marks in fundus photographs, which could help the AI assist the diagnosis of DR in the severe stage.http://dx.doi.org/10.1155/2021/8766517 |
| spellingShingle | Yukang Jiang Jianying Pan Ming Yuan Yanhe Shen Jin Zhu Yishen Wang Yewei Li Ke Zhang Qingyun Yu Huirui Xie Huiting Li Xueqin Wang Yan Luo Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net Journal of Diabetes Research |
| title | Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net |
| title_full | Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net |
| title_fullStr | Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net |
| title_full_unstemmed | Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net |
| title_short | Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net |
| title_sort | segmentation of laser marks of diabetic retinopathy in the fundus photographs using lightweight u net |
| url | http://dx.doi.org/10.1155/2021/8766517 |
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