The co-occurrence of macular hole (MH) and cystoid macular edema (CME) indicates the serious visual impairment in ophthalmology clinic. Joint segmentation and quantitative analysis of MH and CME can greatly assist the ophthalmologists in clinical diagnosis and treatment. Benefitting from the advancement of computer digital image processing technology, deep learning has shown remarkable performance in assisting doctors to diagnose diseases. In this paper, we propose a two-stage network for the segmentation of MH and CME, the MH auxiliary network and the joint segmentation network, in which the output of the Linknet based auxiliary network is used as the input of the joint segmentation network. The MH auxiliary network is designed to solve the problem that the top boundary of the MH is difficult to be discriminated by the joint segmentation network. In the joint segmentation network, we add a mixed downsampling module to retain more fine feature information during the downsampling. Furthermore, a new self-entropy loss function is proposed, which can pay more attention to the hard samples and reduce the uncertainty of the network prediction. Experimental results show that our method achieved an average Dice of 89.32% and an average IOU of 81.42% in segmentation of MH and CME, showing extremely competitive results.
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