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Choroidal atrophy segmentation based on deep network with deep-supervision and EDT-auxiliary-loss

The prevalence of myopia is rapidly increasing worldwide. Along with the deepening of myopia, there will be various pathological changes of retina, such as choroidal atrophy, choroidal neovascularization, etc. In this paper, a U-Net based deep network is proposed to automatically segment choroidal atrophy in fundus images. We use U-Net as the main structure, which can learn rich hierarchical feature representations. In the decoder path, Squeeze-and-Excitation (SE) block is employed before each deconvolution to adaptively recalibrate channel feature response. We introduce deep-supervision mechanism and merge all the early prediction maps to obtain final prediction map. To ensure the smoothness of segmentation results, we propose a new loss function, which is termed EDT-auxiliary-loss (Euclidean distance transformation auxiliary loss). EDT-auxiliary-loss consists of Dice loss for ground truth and mean square error (MSE) loss for distance map. Another strategy for performance improvement is utilizing the information of optic disc (OD), which is usually adjacent to atrophy. The proposed method was evaluated on ISBI 2019 Pathologic Myopia Challenge dataset, which consists of 400 fundus images from161 normal eyes, 26 high myopia eyes and 213 pathologic myopia eyes. The proposed network was validated with four-fold cross validation. The experiment results show that the proposed method can successfully segment choroidal atrophy and achieve better performance than traditional U-Net.