Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation
This work addresses the problem of accurate retinal vessel segmentation for medical imaging applications, with incremental improvements over existing attention U-Net methods.
The paper tackles retinal vessel segmentation by developing a connection sensitive attention U-Net (CSAU) that improves accuracy, particularly for thin vessels and abnormalities, achieving state-of-the-art results on DRIVE, STARE, and HRF datasets.
We develop a connection sensitive attention U-Net(CSAU) for accurate retinal vessel segmentation. This method improves the recent attention U-Net for semantic segmentation with four key improvements: (1) connection sensitive loss that models the structure properties to improve the accuracy of pixel-wise segmentation; (2) attention gate with novel neural network structure and concatenating DOWN-Link to effectively learn better attention weights on fine vessels; (3) integration of connection sensitive loss and attention gate to further improve the accuracy on detailed vessels by additionally concatenating attention weights to features before output; (4) metrics of connection sensitive accuracy to reflect the segmentation performance on boundaries and thin vessels. Our method can effectively improve state-of-the-art vessel segmentation methods that suffer from difficulties in presence of abnormalities, bifurcation and microvascular. This connection sensitive loss tightly integrates with the proposed attention U-Net to accurately (i) segment retinal vessels, and (ii) reserve the connectivity of thin vessels by modeling the structural properties. Our method achieves the leading position on DRIVE, STARE and HRF datasets among the state-of-the-art methods.