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Found 2 papers, 0 papers with code
DUNet: A deformable network for retinal vessel segmentation
Results show that more detailed vessels are extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0. 9697/0. 9722/0. 9724 and AUC of 0. 9856/0. 9868/0. 9863 on DRIVE, STARE and CHASE_DB1 respectively.
Ranked #5 on
Retinal Vessel Segmentation
on STARE
Multipoint Filtering with Local Polynomial Approximation and Range Guidance
By using the hybrid of the local polynomial model and color/intensity based range guidance, the proposed method not only preserves edges but also does a much better job in preserving spatial variation than existing popular filtering methods.
