A framework for predictive modeling of anatomical deformations

Christos Davatzikos, Dinggang Shen, Ashraf Mohamed, Stelios K. Kyriacou

Research output: Contribution to journalLetterpeer-review

71 Citations (Scopus)


A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods are examined. The first is purely shape-based and utilizes the principal modes of co-variation between anatomy and deformation in order to statistically represent deformability. When a patient's anatomy is available, it is used in conjunction with the statistical model to predict the way in which the anatomy will/can deform. The second method is related, and it uses the statistical model in conjunction with a biomechanical model of anatomical deformation. It examines the principal modes of co-variation between shape and forces, with the latter driving the biomechanical model, and thus predicting deformation. Results are shown on simulated images, demonstrating that systematic deformations, such as those resulting from change in position or from tumor growth, can be estimated very well using these models. Estimation accuracy will depend on the application, and particularly on how systematic a deformation of interest is.

Original languageEnglish
Pages (from-to)836-843
Number of pages8
JournalIEEE Transactions on Medical Imaging
Issue number8
Publication statusPublished - 2001 Aug
Externally publishedYes


  • Deformable models
  • Intraoperative deformation
  • Soft tissue deformation
  • Surgical planning

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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