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Abstract

Automatic segmentation of pelvic organs plays a major role in prostate cancer treatment and has high accuracy requirements. Segmentation experts are continuously working on improving their algorithms. However, natural anatomical variability of organs and structures is a common reason for which segmentation algorithms fail. Understanding why an algorithm fails in a specific case is of major importance. Segmentation experts expect that the shape and size of the organs can play an important role in the performance of their algorithms, but current means of exploration and analysis are limited and do not provide the necessary insight. This thesis discusses the design and implementation of a web-based application allowing for easy exploration and analysis of shape variability in order to generate hypotheses about the relation between algorithm performance and shape of organs. A new way of comparatively visualizing multiple organs of multiple patients is introduced for a detailed shape comparison. The application was tested with segmentation meshes of a cohort of 17 patients, each consisting of four pelvic organs and two organ-interfaces, which are labeled and have per-triangle correspondence. The proposed tools already allow users to quickly identify mis-segmented organs and hypothesize about the relation of variability to anatomical features as well as segmentation quality. The approach was applied on pelvic organ segmentations, but it can be extended to other applications like comparison of segmentation algorithms or analysis of anatomical variability in general.

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BibTeX

@mastersthesis{Reiter2017CVAP,
  title =      "Comparative Visualization of Pelvic Organ Segmentations",
  author =     "Oliver Reiter",
  year =       "2018",
  abstract =   "Automatic segmentation of pelvic organs plays a major role
               in prostate cancer treatment and has high accuracy
               requirements. Segmentation experts are continuously working
               on improving their algorithms. However, natural anatomical
               variability of organs and structures is a common reason for
               which segmentation algorithms fail. Understanding why an
               algorithm fails in a specific case is of major importance.
               Segmentation experts expect that the shape and size of the
               organs can play an important role in the performance of
               their algorithms, but current means of exploration and
               analysis are limited and do not provide the necessary
               insight. This thesis discusses the design and implementation
               of a web-based application allowing for easy exploration and
               analysis of shape variability in order to generate
               hypotheses about the relation between algorithm performance
               and shape of organs. A new way of comparatively visualizing
               multiple organs of multiple patients is introduced for a
               detailed shape comparison. The application was tested with
               segmentation meshes of a cohort of 17 patients, each
               consisting of four pelvic organs and two organ-interfaces,
               which are labeled and have per-triangle correspondence. The
               proposed tools already allow users to quickly identify
               mis-segmented organs and hypothesize about the relation of
               variability to anatomical features as well as segmentation
               quality. The approach was applied on pelvic organ
               segmentations, but it can be extended to other applications
               like comparison of segmentation algorithms or analysis of
               anatomical variability in general.",
  month =      mar,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  keywords =   "comparative visualization, statistical shape analysis",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2018/Reiter2017CVAP/",
}