  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 multi-stage 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. These effectively
resolved the issues of varying shapes and artefacts and hence the need for
multiple stages. To assist in better pixel-level classification, we use the
Squeeze and Attention(SA) module, a smartly adapted version of the Squeeze and
Excitation(SE) module for segmentation tasks in our network to facilitate
pixel-group attention. Our unique network design and novel D-DPP module with
efficient task-specific loss function for thin vessels enabled our model for
better cross data performance. Exhaustive experimental results on DRIVE, STARE,
HRF, and CHASE-DB1 show the superiority of our method.
