The paper reports a new annotated 7T ToF MRA dataset for small vessel segmentation and shows that top deep learning methods reach Dice scores of 0.838 on internal test data and 0.716 on an external secret dataset.
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KaLDeX integrates Kalman-filter linear deformable convolution and cross-attention inside UNet++ with persistent-homology loss, reporting higher accuracy than prior models on DRIVE, CHASE_DB1, STARE and OCTA-500 vessel datasets.
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SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms
The paper reports a new annotated 7T ToF MRA dataset for small vessel segmentation and shows that top deep learning methods reach Dice scores of 0.838 on internal test data and 0.716 on an external secret dataset.
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KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation
KaLDeX integrates Kalman-filter linear deformable convolution and cross-attention inside UNet++ with persistent-homology loss, reporting higher accuracy than prior models on DRIVE, CHASE_DB1, STARE and OCTA-500 vessel datasets.