A multitask learning architecture for simultaneous segmentation of bright and red lesions in fundus images

Abstract

Recent CNN architectures have established state-of-the-art results in a large range of medical imaging applications. We propose an extension to the U-Net architecture relying on multi-task learning: while keeping a single encoding module, multiple decoding modules are used for concurrent segmentation tasks. We propose improvements of the encoding module based on the latest CNN developments: residual connections at every scale, mixed pooling for spatial compression and large kernels for convolutions at the lowest scale. We also use dense connections within the different scales based on multi-size pooling regions. We use this new architecture to jointly detect and segment red and bright retinal lesions which are essential biomarkers of diabetic retinopathy. Each of the two categories is handled by a specialized decoding module. Segmentation outputs are refined with conditional random fields (CRF) as RNN and the network is trained end-to-end with an effective Kappa-based function loss. Preliminary results on a public dataset in the segmentation task on red (resp. bright) lesions shows a sensitivity of 66,9% (resp. 75,3%) and a specificity of 99,8% (resp. 99,9%).