W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network

Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a $mathcal{W}$-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the $mathcal{W}$-net. We demonstrated the effectiveness of our approach by applying five-fold cross-validation experiments on two public datasets e_ophtha_EX and DiaRetDb1. We achieved F1-score of 94.76% and 95.73% for OD segmentation, and 92.80% and 94.14% for exudates segmentation. To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation. Our results demonstrated that by choosing the optimal weights of each task, the MTL based $mathcal{W}$-net outperformed separate models trained individually on each task. Code and pre-trained models will be available at: url{https://github.com/FundusResearch/MTL_for_OD_and_exudates.git}.