Peripapillary atrophy (PPA) is a clinical finding that reflects atrophy of the retinal layer and retinal pigment epithelium. It is very important to segment PPA area as it indicates the progress of eye diseases such as myopia and glaucoma, while it is a challenging task to segment PPA due to the irregular and ambiguous boundaries. In this paper, a boundary guidance deep learning method is introduced to segment PPA area to obtain precise shape. We propose a boundary guidance block together with a contour loss function to improve the PPA segmentation performance on boundaries. Our approach is evaluated on a clinical dataset. The F1-score, IOU and Hausdorff distance of our method performance is 80.06%, 67.29%, 5.4934 respectively. Compared with other methods, our method achieves the best performance both qualitatively and quantitatively. Our proposed method can work well on retinal images with narrow PPA even with small training set.
基金：
National Natural Science Foundation of China (NSFC) [82072007]; China Postdoctoral Science Foundation [2020M680387]
语种：
外文
被引次数：
WOS：
第一作者：
第一作者机构：[1]Beijing Inst Technol, Beijing, Peoples R China
通讯作者：
推荐引用方式(GB/T 7714)：
Li Mengxuan,Zhao He,Xu Jie,et al.Peripapillary Atrophy Segmentation with Boundary Guidance[J].OPHTHALMIC MEDICAL IMAGE ANALYSIS, OMIA 2021.2021,12970:101-108.doi:10.1007/978-3-030-87000-3_11.
APA：
Li, Mengxuan,Zhao, He,Xu, Jie&Li, Huiqi.(2021).Peripapillary Atrophy Segmentation with Boundary Guidance.OPHTHALMIC MEDICAL IMAGE ANALYSIS, OMIA 2021,12970,
MLA：
Li, Mengxuan,et al."Peripapillary Atrophy Segmentation with Boundary Guidance".OPHTHALMIC MEDICAL IMAGE ANALYSIS, OMIA 2021 12970.(2021):101-108
