Paper
23 February 2012 Computer-aided mesenteric small vessel segmentation on high-resolution 3D contrast-enhanced CT angiography scans
Weidong Zhang, Jiamin Liu, Jianhua Yao, Tan Nguyen, Adeline Louie, Stephen Wank, Ronald M. Summers
Author Affiliations +
Abstract
Segmentation of the mesenteric vasculature has important applications for evaluation of the small bowel. In particular, it may be useful for small bowel path reconstruction and precise localization of small bowel tumors such as carcinoid. Segmentation of the mesenteric vasculature is very challenging, even for manual labeling, because of the low contrast and tortuosity of the small blood vessels. Many vessel segmentation methods have been proposed. However, most of them are designed for segmenting large vessels. We propose a semi-automated method to extract the mesenteric vasculature on contrast-enhanced abdominal CT scans. First, the internal abdominal region of the body is automatically identified. Second, the major vascular branches are segmented using a multi-linear vessel tracing method. Third, small mesenteric vessels are segmented using multi-view multi-scale vesselness enhancement filters. The method is insensitive to image contrast, variations of vessel shape and small occlusions due to overlapping. The method could automatically detect mesenteric vessels with diameters as small as 1 mm. Compared with the standard-of-reference manually labeled by an expert radiologist, the segmentation accuracy (recall rate) for the whole mesenteric vasculature was 82.3% with a 3.6% false positive rate.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Weidong Zhang, Jiamin Liu, Jianhua Yao, Tan Nguyen, Adeline Louie, Stephen Wank, and Ronald M. Summers "Computer-aided mesenteric small vessel segmentation on high-resolution 3D contrast-enhanced CT angiography scans", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83151T (23 February 2012); https://doi.org/10.1117/12.911841
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Cited by 2 scholarly publications.
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KEYWORDS
Image segmentation

Computed tomography

Abdomen

Angiography

Arteries

Image filtering

Bone

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