SpineRegNet: Spine Registration Network for volumetric MR and CT image by the joint estimation of an affine-elastic deformation field

仿射变换 人工智能 图像配准 计算机科学 椎骨 计算机视觉 刚性变换 刚度(电磁) 数学 医学 图像(数学) 解剖 几何学 物理 量子力学
作者
Lei Zhao,Shumao Pang,Yangfan Chen,Xiongfeng Zhu,Ziyue Jiang,Zhihai Su,Hai Lü,Yujia Zhou,Qianjin Feng
出处
期刊:Medical Image Analysis [Elsevier BV]
卷期号:86: 102786-102786 被引量:18
标识
DOI:10.1016/j.media.2023.102786
摘要

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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