A survey on statistical modeling and machine learning approaches to computer assisted medical intervention: Intraoperative anatomy modeling and optimization of interventional procedures

Ken'ichi Morooka, Masahiko Nakamoto, Yoshinobu Sato

Research output: Contribution to journalReview article

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)784-797
Number of pages14
JournalIEICE Transactions on Information and Systems
Issue number4
Publication statusPublished - Apr 2013


All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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