期刊
APPLIED SOFT COMPUTING
卷 86, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2019.105890
关键词
Deep learning; Medical image segmentation; Peripapillary atrophy segmentation; Convolutional neural networks; Fully convolutional network
资金
- National Natural Science Foundation of China (NSFC) [71771131, 71432004, 71490724, 81700882]
- priming scientific research foundation for the researcher in Beijing Tongren Hospital, China [2017-YJJ-GGL-009, 2018-YJJ-ZZL-052]
Peripapillary atrophy (PPA) is a clinical finding, which reflects the atrophy of retina layer and retinal pigment epithelium. The size of PPA area is a useful medical indicator, as it is highly associated with many diseases such as glaucoma and myopia. Therefore, separating the PPA area from retinal images, which is called PPA area segmentation, is very important. It is a challenging task, because PPA areas are irregular and non-uniform, and their contours are blurry and change gradually. To solve these issues, we transform the PPA area segmentation task into a task of segmenting another two areas with relatively regular and uniform shapes, and then propose a novel multi-task fully convolutional network (MFCN) model to jointly extract them from retinal images. Meanwhile, we take edge continuity of the target area into consideration. To evaluate the performance of the proposed model, we conduct experiments on images with PPA areas labelled by experts and achieve an average precision of 0.8928, outperforming the state-of-the-art models. To demonstrate the application of PPA segmentation in medical research, we apply PPA related features based on the segmented PPA area on differentiating glaucomatous and physiologic large cup cases. Experiment conducted on real datasets confirms the effectiveness of using these features for glaucoma diagnosis. (C) 2019 Elsevier B.V. All rights reserved.
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