Smooth extrapolation of unknown anatomy via statistical shape models

R. B. Grupp, H. Chiang, Y. Otake, R. J. Murphy, C. R. Gordon, M. Armand, R. H. Taylor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Several methods to perform extrapolation of unknown anatomy were evaluated. The primary application is to enhance surgical procedures that may use partial medical images or medical images of incomplete anatomy. Le Fort-based, face-jaw-teeth transplant is one such procedure. From CT data of 36 skulls and 21 mandibles separate Statistical Shape Models of the anatomical surfaces were created. Using the Statistical Shape Models, incomplete surfaces were projected to obtain complete surface estimates. The surface estimates exhibit non-zero error in regions where the true surface is known; it is desirable to keep the true surface and seamlessly merge the estimated unknown surface. Existing extrapolation techniques produce non-smooth transitions from the true surface to the estimated surface, resulting in additional error and a less aesthetically pleasing result. The three extrapolation techniques evaluated were: copying and pasting of the surface estimate (non-smooth baseline), a feathering between the patient surface and surface estimate, and an estimate generated via a Thin Plate Spline trained from displacements between the surface estimate and corresponding vertices of the known patient surface. Feathering and Thin Plate Spline approaches both yielded smooth transitions. However, feathering corrupted known vertex values. Leave-one-out analyses were conducted, with 5% to 50% of known anatomy removed from the left-out patient and estimated via the proposed approaches. The Thin Plate Spline approach yielded smaller errors than the other two approaches, with an average vertex error improvement of 1.46 mm and 1.38 mm for the skull and mandible respectively, over the baseline approach.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2015
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsRobert J. Webster, Ziv R. Yaniv
PublisherSPIE
ISBN (Electronic)9781628415056
DOIs
StatePublished - Jan 1 2015
EventMedical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling - Orlando, United States
Duration: Feb 22 2015Feb 24 2015

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9415
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CityOrlando
Period2/22/152/24/15

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Keywords

  • Anatomical atlas
  • Extrapolation
  • Model completion
  • Statistical Shape Model

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Grupp, R. B., Chiang, H., Otake, Y., Murphy, R. J., Gordon, C. R., Armand, M., & Taylor, R. H. (2015). Smooth extrapolation of unknown anatomy via statistical shape models. In R. J. Webster, & Z. R. Yaniv (Eds.), Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling [941524] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9415). SPIE. https://doi.org/10.1117/12.2081310