摘要
Abstract
The KAN(Kolmogorov-Arnold Networks)model enables the accuracy of image segmentation to be im-proved by a new linear function fitting method.However,the problems of single fitting angle and poor extraction of label po-sition information lead to its poor ability to process the detailed feature information of labels,which limits the improvement of network accuracy.To address the above problems,a multi-scale dual-channel 3D image segmentation model is designed,which significantly enhances the network's ability to extract minute features from images by integrating multi-angle 3D im-age inputs and combining the multi-angle KAN module with multi-scale convolutional weighted residual channels.In terms of the network attention mechanism,a multi-view self-attentive residual module is designed,which effectively captures the label spatial location information through multi-dimensional feature interactions,so that the label region with a relatively low percentage(<10%)can still maintain excellent segmentation accuracy.The model is experimented on BraTS2021 MRI multi-modal 3D brain tumor dataset and LiTS2017 liver tumor CT 3D dataset.The accuracy of the improved model is 86.54%and 88.07%,respectively;in the brain tumor dataset,the Dice evaluation indexes of the enhanced tumor,all tumors,and tumor core region reach 83.67%,88.79%,and 85.28%,which are improved by 3.38,2.85,and 1.62 percentage points,respectively,compared with the U-KAN network;in the liver tumor dataset,the liver and tumor region's Dice evaluation index reached 91.36%and 84.77%,which were improved by 1.69 percentage points and 1.02 percentage points,respectively.The experi-mental results show that the model improves the effect of 3D tumor image segmentation significantly.关键词
三维影像分割/多角度/双通道/自注意力/标签区域强化Key words
3D image segmentation/multi-angle/dual-channel/self-attention/labeled region reinforcement分类
信息技术与安全科学
