By the same authors

Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours

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

Published copy (DOI)

Author(s)

  • Alexander Wood
  • S M Reza Soroushmehr
  • Negar Farzaneh
  • David Fessell
  • Kevin R Ward
  • Jonathan Gryak
  • Delaram Kahrobaei
  • Kayvan Na

Department/unit(s)

Publication details

Title of host publication2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DatePublished - Jul 2018
Pages53-56
Number of pages4
PublisherIEEE
Volume2018
Original languageEnglish

Publication series

NameConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
ISSN (Print)1557-170X

Abstract

Automated segmentation of the spleen in CT volumes is difficult due to variations in size, shape, and position of the spleen within the abdominal cavity as well as similarity of intensity values among organs in the abdominal cavity. In this paper we present a method for automated localization and segmentation of the spleen within axial abdominal CT volumes using trained classification models, active contours, anatomical information, and adaptive features. The results show an average Dice score of 0.873 on patients experiencing various chest, abdominal, and pelvic traumas taken at different contrast phases.

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