(M)SLAe-Net: Multi-Scale Multi-Level Attention embedded Network for Retinal Vessel Segmentation

Shreshth Saini

Abstract: Segmentation plays a crucial role in diagnosis. Studying the retinal vasculatures from fundus images help identify early signs of many crucial illnesses such as diabetic retinopathy. Due to the varying shape, size, and patterns of retinal vessels, along with artefacts and noises in fundus images, no one-stage method can accurately segment retinal vessels. In this work, we propose a multi-scale, multi-level attention embedded CNN architecture ((M)SLAe-Net) to address the issue of multistage processing for robust and precise segmentation of retinal vessels. We do this by extracting features at multiple scales and multiple levels of the network, enabling our model to holistically extracts the local and global features. Multi-scale features are extracted using our novel dynamic dilated pyramid pooling (D - DPP) module. We also aggregate the features from all the network levels.