Choroidal Neovascularization Segmentation Combining Temporal Supervision and Attention Mechanism - YE Zhong-yu, WU Meng-lin (Jisuanji kexue, 2021)
Choroidal neovascularization (CNV) generally occurs at the late stage of senile macular degeneration (AMD),and accurate segmentation of CNV in optical coherence tomography (SD-OCT) is of great significance for the diagnosis and treatment of AMD.This paper proposes a CNV multi-task segmentation network that combines time series model and attention mechanism.The continuous SD-OCT image is input into the segmentation network,and the multi-scale information of the picture is extracted in the encoder part.In order to better extract the local features of the picture,the attention gate is added in the skip connection part.In order to solve the problem of discontinuous scanning segmentation,after the segmentation network is pooled,the timing constraint network is passed to generate the continuity constraint of adjacent frames and gradient constraints are added to the loss function to better preserve the lesion boundary.The spatial pyramid is used to fuse the two parts of the network feature map to produce segmentation loss,which improves the final segmentation accuracy.Based on patient independence,effective cross-validation is performed on 200 eyes of 12 patients.The Dice coefficient reaches 76.3% and the overlap reaches 60.7%.CNV can be reliably segmented in SD-OCT images.