文献类型：期刊文献

英文题名：Structure-aware independently trained multi-scale registration network for cardiac images

作者：Chang, Qing[1];Wang, Yaqi[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

年份：2024

卷号：62

期号：6

起止页码：1795

外文期刊名：MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING

收录：;EI(收录号：20240815611876);WOS:【SCI-EXPANDED(收录号:WOS:001169276100001)】；

基金：No Statement Available

语种：英文

外文关键词：Image registration; Cardiac image; Deep learning; Deformable registration

摘要：Image registration is a primary task in various medical image analysis applications. However, cardiac image registration is difficult due to the large non-rigid deformation of the heart and the complex anatomical structure. This paper proposes a structure-aware independently trained multi-scale registration network (SIMReg) to address this challenge. Using image pairs of different resolutions, independently train each registration network to extract image features of large deformation image pairs at different resolutions. In the testing stage, the large deformation registration is decomposed into a multi-scale registration process, and the deformation fields of different resolutions are fused by a step-by-step deformation method, thus solving the difficulty of directly processing large deformation. Meanwhile, the targeted introduction of MIND (modality independent neighborhood descriptor) structural features to guide network training enhances the registration of cardiac structural contours and improves the registration effect of local details. Experiments were carried out on the open cardiac dataset ACDC (automated cardiac diagnosis challenge), and the average Dice value of the experimental results of the proposed method was 0.833. Comparative experiments showed that the proposed SIMReg could better solve the problem of heart image registration and achieve a better registration effect on cardiac images.