Simultaneous segmentation and correspondence improvement using statistical modes

Ayushi Sinha, Austin Reiter, Simon Leonard, Masaru Ishii, Gregory D. Hager, Russell H. Taylor

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

Abstract

With the increasing amount of patient information that is being collected today, the idea of using this information to inform future patient care has gained momentum. In many cases, this information comes in the form of medical images. Several algorithms have been presented to automatically segment these images, and to extract structures relevant to different diagnostic or surgical procedures. Consequently, this allows us to obtain large data-sets of shapes, in the form of triangular meshes, segmented from these images. Given correspondences between these shapes, statistical shape models (SSMs) can be built using methods like Principal Component Analysis (PCA). Often, the initial correspondences between the shapes need to be improved, and SSMs can be used to improve these correspondences. However, just as often, initial segmentations also need to be improved. Unlike many correspondence improvement algorithms, which do not affect segmentation, many segmentation improvement algorithms negatively affect correspondences between shapes. We present a method that iteratively improves both segmentation as well as correspondence by using SSMs not only to improve correspondence, but also to constrain the movement of vertices during segmentation improvement. We show that our method is able to maintain correspondence while achieving as good or better segmentations than those produced by methods that improve segmentation without maintaining correspondence. We are additionally able to achieve segmentations with better triangle quality than segmentations produced without correspondence improvement.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationImage Processing
EditorsElsa D. Angelini, Martin A. Styner, Elsa D. Angelini
PublisherSPIE
ISBN (Electronic)9781510607118
DOIs
StatePublished - 2017
EventMedical Imaging 2017: Image Processing - Orlando, United States
Duration: Feb 12 2017Feb 14 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10133
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2017: Image Processing
CountryUnited States
CityOrlando
Period2/12/172/14/17

Keywords

  • Correspondence improvement
  • Segmentation improvement
  • Statistical shape model

ASJC Scopus subject areas

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

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