SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies
This work addresses vessel segmentation in medical imaging, an incremental advance with specific gains for hepatic analysis.
The paper tackles the challenge of segmenting hepatic vessels in CT scans, which suffer from fragmentation and noise, by proposing SegKAN, a model that improves embedding and captures long-distance dependencies, resulting in a 1.78% Dice score improvement over the state-of-the-art.
Hepatic vessels in computed tomography scans often suffer from image fragmentation and noise interference, making it difficult to maintain vessel integrity and posing significant challenges for vessel segmentation. To address this issue, we propose an innovative model: SegKAN. First, we improve the conventional embedding module by adopting a novel convolutional network structure for image embedding, which smooths out image noise and prevents issues such as gradient explosion in subsequent stages. Next, we transform the spatial relationships between Patch blocks into temporal relationships to solve the problem of capturing positional relationships between Patch blocks in traditional Vision Transformer models. We conducted experiments on a Hepatic vessel dataset, and compared to the existing state-of-the-art model, the Dice score improved by 1.78%. These results demonstrate that the proposed new structure effectively enhances the segmentation performance of high-resolution extended objects. Code will be available at https://github.com/goblin327/SegKAN