Paper
27 February 2009 Automated labeling of anatomic segments of the colon in CT colonography
Author Affiliations +
Abstract
CT colonography is a minimally invasive technique that can be used to find polyps and malignant tumors in the colon. However, if a polyp or malignant tumor is found, a colonoscopy is then required to further investigate and remove it. One major problem in relaying the location of a polyp between radiologists and colonoscopists is the ambiguity of the divisions between various colon segments. Because there exists no concrete separator between segments, miscommunication of polyp locations can result. In an effort to minimize such miscommunications, an automated labeling program has been created. This program reads in CT images and returns physical coordinates of the divisions between segments. Such a system would allow for a more universally accepted method for communication of polyp location between radiologists and colonoscopists, and hopefully increase the speed and ease with which such polyp location can be reported. The purpose of this study was to validate the automated method of labeling by comparing physical coordinates of region dividers found using the program with those manually determined by a radiologist. The segments were defined with a modified version of a procedure developed by Taylor et al (Radiology 229:99-108, 2003). A set of 30 scans was used to train the system and then a test set of 216 cases was used to validate the system. The system reported locations that averaged 1-3 cm different than manually reported locations. The errors are on the order of the diameters of the colonic segments and are in the clinically acceptable range.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Patric J. Glynn and Ronald M. Summers "Automated labeling of anatomic segments of the colon in CT colonography", Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 72622O (27 February 2009); https://doi.org/10.1117/12.812042
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Colon

Image segmentation

Virtual colonoscopy

Computed tomography

Telecommunications

Tumors

Radiology

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