SpineRegNet: Spine Registration Network for volumetric MR and CT image by the joint estimation of an affine-elastic deformation field |
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| Affiliation: | 1. Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany;2. National Center for Tumor Diseases (NCT) Heidelberg, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany;3. Data Assisted Solutions, Corporate Research & Technology, KARL STORZ SE & Co. KG, Dr. Karl-Storz-Str. 34, 78332 Tuttlingen;4. Div. Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120 Heidelberg Germany;5. HIP Helmholtz Imaging Platform, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120 Heidelberg Germany;6. Faculty of Mathematics and Computer Science, Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg;7. Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 280, Heidelberg, Germany;8. ICube, University of Strasbourg, CNRS, France. 300 bd Sébastien Brant - CS 10413, F-67412 Illkirch Cedex, France;9. IHU Strasbourg, France. 1 Place de l''hôpital, 67000 Strasbourg, France;10. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children''s National Hospital, 111 Michigan Ave NW, Washington, DC 20010, USA;11. University of Maryland, College Park, 2405 A V Williams Building, College Park, MD 20742, USA;12. Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Str. 2, 28359 Bremen, Germany;13. University of Bremen, FB3, Medical Image Computing Group, ℅ Fraunhofer MEVIS, Am Fallturm 1, 28359 Bremen, Germany;14. Lab for Medical Imaging and Digital Surgery, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;15. Konika Minolta, Inc., 1-2, Sakura-machi, Takatsuki, Oasak 569-8503, Japan;p. Wintegral GmbH, Ehrenbreitsteiner Str. 36, 80993 München, Germany;q. Department of Computer Science and Engineering, Ho Sin-Hang Engineering Building, The Chinese University of Hong Kong, Sha Tin, NT, Hong Kong;r. Caresyntax GmbH, Komturstr. 18A, 12099 Berlin, Germany;s. Hikvision Research Institute, Hangzhou, China;t. School of Computing, National University of Singapore, Computing 1, No.13 Computing Drive, 117417, Singapore;u. National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China;v. Department of Surgery, Salem Hospital of the Evangelische Stadtmission Heidelberg, Zeppelinstrasse 11-33, 69121 Heidelberg, Germany;w. Health Robotics and Automation Laboratory, Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Geb. 40.28, KIT Campus Süd, Engler-Bunte-Ring 8, 76131 Karlsruhe, Germany;x. Medical Faculty, Heidelberg University, Im Neuenheimer Feld 672, 69120 Heidelberg;y. Div. Translational Surgical Oncology, National Center for Tumor Diseases Dresden, Fetscherstraße 74, 01307 Dresden, Germany;z. Cluster of Excellence “Centre for Tactile Internet with Human-in-the-Loop” (CeTI) of Technische Universität Dresden, 01062 Dresden, Germany |
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| 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. |
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| Keywords: | Deep learning Spine registration Affine-elastic registration Local rigidity constrain |
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