Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net
| Autor: | Huiting Li, Jianying Pan, Yan Luo, Yewei Li, Qingyun Yu, Yukang Jiang, Xueqin Wang, Yanhe Shen, Jin Zhu, Ming Yuan, Ke Zhang, Huirui Xie, Yishen Wang |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Article Subject
genetic structures Computer science Fundus Oculi Endocrinology Diabetes and Metabolism Fundus (eye) Light Coagulation Panretinal photocoagulation Severity of Illness Index Accurate segmentation Diseases of the endocrine glands. Clinical endocrinology law.invention Cicatrix Endocrinology Deep Learning law medicine Image Processing Computer-Assisted Photography Humans Computer vision Segmentation Diabetic Retinopathy business.industry Diabetic retinopathy medicine.disease Laser RC648-665 Artificial intelligence business Research Article |
| Zdroj: | Journal of Diabetes Research, Vol 2021 (2021) Journal of Diabetes Research |
| ISSN: | 2314-6753 2314-6745 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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