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

Lei Zhao; Shumao Pang; Yangfan Chen; Xiongfeng Zhu; Ziyue Jiang; Zhihai Su; Hai Lu; Yujia Zhou; Qianjin Feng

doi:10.1016/j.media.2023.102786

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

Med Image Anal. 2023 May:86:102786. doi: 10.1016/j.media.2023.102786. Epub 2023 Mar 1.

Authors

Lei Zhao¹, Shumao Pang¹, Yangfan Chen¹, Xiongfeng Zhu¹, Ziyue Jiang², Zhihai Su³, Hai Lu³, Yujia Zhou⁴, Qianjin Feng⁵

Affiliations

¹ School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China.
² Department of Orthopedics, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China.
³ Department of Spinal Surgery, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000, China.
⁴ School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China. Electronic address: yujia90@smu.edu.cn.
⁵ School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China. Electronic address: fengqj99@smu.edu.cn.

PMID: 36878160
DOI: 10.1016/j.media.2023.102786

Abstract

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.

Keywords: Affine-elastic registration; Deep learning; Local rigidity constrain; Spine registration.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Humans
Image Processing, Computer-Assisted / methods
Intervertebral Disc*
Magnetic Resonance Imaging / methods
Magnetic Resonance Spectroscopy
Tomography, X-Ray Computed / methods