Semiautomated editing of computed tomography sections for visualization of vasculature

Smadar Shiffman, Geoffrey D. Rubin, Sandy Napel

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations


The goal of our work is to help radiologists remove obscuring structures from a large volume of computed tomography angiography (CTA) images by editing a small number of sections prior to three-dimensional (3D) reconstruction. We combine automated segmentation of the entire volume with manual editing of a small number of sections. The segmentation process uses a neural network to learn thresholds for multilevel thresholding and a constraint- satisfaction neural network to smooth the boundaries of labeled segments. Following segmentation, the user edits a small number of images by pointing and clicking, and then a connectivity procedure automatically selects corresponding segments from other sections by comparing adjacent voxels within and across sections for label identity. Our results suggest that automated segmentation followed by minimal manual editing is a promising approach to editing of CTA sequences. However, prerequisites to clinical utility are evaluation of segmentation accuracy and development of methods for resolution of label ambiguity.

Original languageEnglish (US)
Pages (from-to)140-151
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 1996
Externally publishedYes
EventMedical Imaging 1996: Image Display - Newport Beach, CA, United States
Duration: Feb 11 1996Feb 11 1996


  • CT angiography
  • Image segmentation
  • Neural networks
  • Semiautomated editing of images

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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