A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

• MOROOKA Ken'ichi

  Graduate School of Information Science and Electrical Engineering, Kyushu University

• NAKAMOTO Masahiko

  Graduate School of Medicine, Osaka University

• SATO Yoshinobu

  Graduate School of Medicine, Osaka University

Abstract

This paper reviews methods for computer assisted medical intervention using statistical models and machine learning technologies, which would be particularly useful for representing prior information of anatomical shape, motion, and deformation to extrapolate intraoperative sparse data as well as surgeons' expertise and pathology to optimize interventions. Firstly, we present a review of methods for recovery of static anatomical structures by only using intraoperative data without any preoperative patient-specific information. Then, methods for recovery of intraoperative motion and deformation are reviewed by combining intraoperative sparse data with preoperative patient-specific stationary data, which is followed by a survey of articles which incorporated biomechanics. Furthermore, the articles are reviewed which addressed the used of statistical models for optimization of interventions. Finally, we conclude the survey by describing the future perspective.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E96.D (4), 784-797, 2013

  The Institute of Electronics, Information and Communication Engineers

Keywords

• statistical shape model
• statistical deformation model
• preoperating planning
• surgical navigation
• anatomical shape
• organ motion
• tissue deformation

Details

• CRID: 1390001204378208256
• NII Article ID: 10031182843
• NII Book ID: AA10826272
• DOI: 10.1587/transinf.e96.d.784
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E96.D/4/E96.D_784/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed