西北工程技术学报2025，Vol.24Issue(2)：137-145,9.
基于深度可变形配准的多图谱海马体图像分割
Multi-Atlas Hippocampus Image Segmentation Based on Depth Deformable Registration
摘要
Abstract
Aiming at the problem of low accuracy of the hippocampus image multi-atlas segmentation algorithm,a depth deformable registration model based on U-Net is proposed in the registration stage of multi-atlas segmentation.The standard convolution of the U-Net encoding session is replaced with depthwise separable convolution to enhance the feature extraction ability of the model.A deformable large kernel attention(D-LKA)module is introduced to improve the attention to important regional features.By utilizing the dilated convolution module to expand the receptive field,the model strengthens its ability to capture multi-scale information.Experimental results of the proposed algorithm on public available datasets LPBA40 and OASIS show that the model registration accuracy on the OASIS dataset achieves 0.798 8,and the final segmentation accuracy is improved by 5%-9%in comparison with other registration methods through the majority voting method in the label fusion stage of multi-atlas segmentation.This model demonstrates potential clinical application value and offers valuable insights in early Alzheimer's disease diagnosis.关键词
多图谱分割/海马体/图像配准/标签融合/深度可分离卷积/空洞卷积Key words
multi-atlas segmentation/hippocampus/image registration/label fusion/depthwise separable convolution/dilated convolution分类
信息技术与安全科学引用本文复制引用
张静,马瑜,巫睿阳,肖博文..基于深度可变形配准的多图谱海马体图像分割[J].西北工程技术学报,2025,24(2):137-145,9.基金项目
国家自然科学基金项目(42361056) （42361056）
中央支持地方专项资金项目(2023FRD05034) （2023FRD05034）
宁夏重点研发计划高新技术领域项目(2023BDE03002) （2023BDE03002）
