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Title page for ETD etd-01192016-152253

Type of Document Dissertation
Author Xu, Zhoubing
Author's Email Address zhoubing.xu@vanderbilt.edu
URN etd-01192016-152253
Title Automatic Segmentation of the Human Abdomen on Clinically Acquired CT
Degree PhD
Department Electrical Engineering
Advisory Committee
Advisor Name Title
Bennett A. Landman Committee Chair
Benjamin K. Poulose Committee Member
Benoit M. Dawant Committee Member
Jack H. Noble Committee Member
Richard G. Abramson Committee Member
  • active shape model
  • multi-atlas label fusion
  • image segmentation
  • abdomen
  • CT
  • quantitative analysis
Date of Defense 2015-12-09
Availability unrestricted
The human abdomen is an essential, yet complex body space clinically. Computational tomography (CT) scans are routinely taken for the diagnosis and prognosis of abdomen-related diseases, such as the pathological injuries or changes of abdominal organs, and the abnormal extrusion through the abdominal wall. Segmentation on CT images provides a computational representation for the structures of interest to access the structural characteristics, and thus establishes a foundation for quantitative analysis. While fully automated segmentation on large-scale clinical imaging data has been the target of intense efforts for decades, robust segmentation systems for the abdomen remain elusive.

Here, we present automatic segmentation approaches for (1) the abdominal wall (covering both outer and inner surfaces over the range between xiphoid process and pubic symphysis) and (2) multiple abdominal organs (up to 13 organs, including liver, spleen, and kidneys) on clinically acquired CT. State-of-the-art atlas- and surface-based image processing techniques are investigated and robustly adapted to the challenging problems in abdomen given (a) anatomical structures with substantial occurrences of abnormalities and large variations in shape and appearance, and (b) CT scans with varied sizes and resolutions, fields of view, contrast enhancement, and imaging artifacts. Translational studies are performed to demonstrate the efficacy of the presented segmentation to support clinical decisions.

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