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arXiv Computer Vision By Shengbo Tan, Rundong Xue, Shipeng Luo, Zeyu Zhang, Xinran Wang, Lei Zhang, Daji Ergu, Zhang Yi, Yang Zhao, Ying Cai

SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

Read the original on arXiv Computer Vision →

SegKAN is a new model for high‑resolution medical image segmentation that tackles fragmentation and noise in hepatic vessel CT scans. It replaces the standard embedding module with a novel convolutional network to smooth noise and avoid gradient explosion, and reinterprets spatial relationships between Patch blocks as temporal relationships to better capture positional dependencies. Experiments on a hepatic vessel dataset show a 1.78% improvement in Dice score over the current state‑of‑the‑art model, indicating that the new structure enhances segmentation performance for extended objects.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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