文献类型：期刊文献

英文题名：Independently Trained Multi-Scale Registration Network Based on Image Pyramid

作者：Chang, Qing[1];Wang, Yaqi[1];Zhang, Jieming[1]

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

年份：2024

卷号：37

期号：4

起止页码：1557

外文期刊名：JOURNAL OF IMAGING INFORMATICS IN MEDICINE

收录：;EI(收录号：20252418595584);WOS:【SCI-EXPANDED(收录号:WOS:001284805400030)】；

基金：No Statement Available

语种：英文

外文关键词：Medical image registration; Cardiac image; Deep learning; Large deformation

摘要：Image registration is a fundamental task in various applications of medical image analysis and plays a crucial role in auxiliary diagnosis, treatment, and surgical navigation. However, cardiac image registration is challenging due to the large non-rigid deformation of the heart and the complex anatomical structure. To address this challenge, this paper proposes an independently trained multi-scale registration network based on an image pyramid. By down-sampling the original input image multiple times, we can construct image pyramid pairs, and design a multi-scale registration network using image pyramid pairs of different resolutions as the training set. Using image pairs of different resolutions, train each registration network independently to extract image features from the image pairs at different resolutions. During the testing stage, the large deformation registration is decomposed into a multi-scale registration process. The deformation fields of different resolutions are fused by a step-by-step deformation method, thereby addressing the challenge of directly handling large deformations. Experiments were conducted on the open cardiac dataset ACDC (Automated Cardiac Diagnosis Challenge); the proposed method achieved an average Dice score of 0.828 in the experimental results. Through comparative experiments, it has been demonstrated that the proposed method effectively addressed the challenge of heart image registration and achieved superior registration results for cardiac images.