Multi-task Learning Based Ocular Disease Discrimination and FAZ Segmentation Utilizing OCTA Images

In this paper, we proposed and validated a multi-task based deep learning method for simultaneously segmenting the foveal avascular zone (FAZ) and classifying three ocular disease related states (normal, diabetic, and myopia) utilizing optical coherence tomography angiography (OCTA) images. The essential motivation of this work is that reliable predictions on disease states may be made based on features extracted from a segmentation network, by sharing a same encoder between the classification network and the segmentation network. In this study, a cotraining network structure was designed for simultaneous ocular disease discrimination and FAZ segmentation. Specifically, we made use of a classification head following a segmentation network's encoder, so that the classification branch used the feature information extracted in the segmentation branch to improve the classification results. The performance of our proposed network structure has been tested and validated on the FAZID dataset, with the best Dice and Jaccard being 0.9031 +/- 0.0772 and 0.8302 +/- 0.0990 for FAZ segmentation, and the best Accuracy and Kappa being 0.7533 and 0.6282 for classifying three ocular disease related states.