Smooth extrapolation of unknown anatomy via statistical shape models

R. B. Grupp, H. Chiang, Y. Otake, R. J. Murphy, Chad R Gordon, Mehran Armand, Russell 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: Image-Guided Procedures, Robotic Interventions, and Modeling
PublisherSPIE
Volume9415
ISBN (Print)9781628415056
DOIs
StatePublished - 2015
EventMedical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling - Orlando, United States
Duration: Feb 22 2015Feb 24 2015

Other

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

Fingerprint

anatomy
Statistical Models
Extrapolation
extrapolation
Anatomy
Mandible
Skull
feathering
Jaw
Tooth
thin plates
splines
estimates
Transplants
Splines
skull
apexes
Copying
teeth

Keywords

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

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • 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 Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling (Vol. 9415). [941524] SPIE. https://doi.org/10.1117/12.2081310

Smooth extrapolation of unknown anatomy via statistical shape models. / Grupp, R. B.; Chiang, H.; Otake, Y.; Murphy, R. J.; Gordon, Chad R; Armand, Mehran; Taylor, Russell H.

Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling. Vol. 9415 SPIE, 2015. 941524.

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

Grupp, RB, Chiang, H, Otake, Y, Murphy, RJ, Gordon, CR, Armand, M & Taylor, RH 2015, Smooth extrapolation of unknown anatomy via statistical shape models. in Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling. vol. 9415, 941524, SPIE, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, Orlando, United States, 2/22/15. https://doi.org/10.1117/12.2081310
Grupp RB, Chiang H, Otake Y, Murphy RJ, Gordon CR, Armand M et al. Smooth extrapolation of unknown anatomy via statistical shape models. In Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling. Vol. 9415. SPIE. 2015. 941524 https://doi.org/10.1117/12.2081310
Grupp, R. B. ; Chiang, H. ; Otake, Y. ; Murphy, R. J. ; Gordon, Chad R ; Armand, Mehran ; Taylor, Russell H. / Smooth extrapolation of unknown anatomy via statistical shape models. Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling. Vol. 9415 SPIE, 2015.
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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.",
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