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Proceedings Paper

Semiautomated editing of computed tomography sections for visualization of vasculature
Author(s): Smadar Shiffman; Geoffrey D. Rubin M.D.; Sandy Napel
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Paper Abstract

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.

Paper Details

Date Published: 15 April 1996
PDF: 12 pages
Proc. SPIE 2707, Medical Imaging 1996: Image Display, (15 April 1996); doi: 10.1117/12.238441
Show Author Affiliations
Smadar Shiffman, Stanford Univ. School of Medicine (United States)
Geoffrey D. Rubin M.D., Stanford Univ. School of Medicine (United States)
Sandy Napel, Stanford Univ. School of Medicine (United States)

Published in SPIE Proceedings Vol. 2707:
Medical Imaging 1996: Image Display
Yongmin Kim, Editor(s)

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