期刊
MEDICAL IMAGE ANALYSIS
卷 86, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.media.2023.102786
关键词
Deep learning; Spine registration; Affine-elastic registration; Local rigidity constrain
SpineRegNet is a novel affine-elastic registration framework that achieves spine image registration through a Multiple Affine Matrices Estimation (MAME) Module, an Affine-Elastic Fusion (AEF) Module, and a Local Rigidity Constraint (LRC) Module. Experimental results show that the proposed approach has impressive performance in spine image registration.
Spine registration for volumetric magnetic resonance (MR) and computed tomography (CT) images plays a significant role in surgical planning and surgical navigation system for the radiofrequency ablation of spine intervertebral discs. The affine transformation of each vertebra and elastic deformation of the intervertebral disc exist at the same time. This situation is a major challenge in spine registration. Existing spinal image registration methods failed to solve the optimal affine-elastic deformation field (AEDF) simultaneously, only consider the overall rigid or elastic alignment with the help of a manual spine mask, and encounter difficulty in meeting the accuracy requirements of clinical registration application. In this study, we propose a novel affine-elastic registration framework named SpineRegNet. The SpineRegNet consists of a Multiple Affine Matrices Estimation (MAME) Module for multiple vertebrae alignment, an Affine-Elastic Fusion (AEF) Module for joint estimation of the overall AEDF, and a Local Rigidity Constraint (LRC) Module for preserving the rigidity of each vertebra. Experiments on T2-weighted volumetric MR and CT images show that the proposed approach achieves impressive performance with mean Dice similarity coefficients of 91.36%, 81.60%, and 83.08% for the mask of the vertebrae on Datasets A-C, respectively. The proposed technique does not require a mask or manual participation during the tests and provides a useful tool for clinical spinal disease surgical planning and surgical navigation systems.
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