W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network
This work addresses the need for efficient and accurate segmentation in medical image analysis for diagnosing retinal diseases, but it is incremental as it builds on existing multi-task learning methods.
The paper tackled the problem of simultaneously segmenting multiple anatomical structures in retinal images, specifically the optic disc and exudates, using a multi-task deep neural network called W-net, achieving F1-scores up to 95.73% for optic disc and 94.14% for exudates on public datasets.
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}.