提出SegKAN模型，提升肝脏血管高分辨率分割精度
SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies
- 用新型卷积结构平滑噪声并稳定梯度
- 将图像块空间关系转为时间关系，捕捉长距离依赖
- 在肝血管数据集上Dice分数提升1.78%，适合医学图像分割任务
CT扫描中的肝血管常因图像碎片化和噪声干扰导致结构断裂，难以保持完整性，给分割带来挑战。为此，我们提出一种创新模型SegKAN。首先，通过新型卷积网络结构改进传统嵌入模块，有效平滑图像噪声，避免后续阶段出现梯度爆炸问题。其次，将图像块间的空间关系转化为时间关系，解决传统Vision Transformer模型难以捕捉图像块间远距离位置关系的问题。在肝血管数据集上的实验表明，相比现有最先进模型，该方法的Dice分数提升了1.78%。结果证明，所提结构能有效提升对高分辨率长距离物体的分割性能。代码将开源于https://github.com/goblin327/SegKAN。
原文摘要 · Abstract (English)
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
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