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23 February 2012 Computer-aided marginal artery detection on computed tomographic colonography
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Abstract
Computed tomographic colonography (CTC) is a minimally invasive technique for colonic polyps and cancer screening. The marginal artery of the colon, also known as the marginal artery of Drummond, is the blood vessel that connects the inferior mesenteric artery with the superior mesenteric artery. The marginal artery runs parallel to the colon for its entire length, providing the blood supply to the colon. Detecting the marginal artery may benefit computer-aided detection (CAD) of colonic polyp. It can be used to identify teniae coli based on their anatomic spatial relationship. It can also serve as an alternative marker for colon localization, in case of colon collapse and inability to directly compute the endoluminal centerline. This paper proposes an automatic method for marginal artery detection on CTC. To the best of our knowledge, this is the first work presented for this purpose. Our method includes two stages. The first stage extracts the blood vessels in the abdominal region. The eigenvalue of Hessian matrix is used to detect line-like structures in the images. The second stage is to reduce the false positives in the first step. We used two different masks to exclude the false positive vessel regions. One is a dilated colon mask which is obtained by colon segmentation. The other is an eroded visceral fat mask which is obtained by fat segmentation in the abdominal region. We tested our method on a CTC dataset with 6 cases. Using ratio-of-overlap with manual labeling of the marginal artery as the standard-of-reference, our method yielded true positive, false positive and false negative fractions of 89%, 33%, 11%, respectively.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhuoshi Wei, Jianhua Yao, Shijun Wang, Jiamin Liu, and Ronald M. Summers "Computer-aided marginal artery detection on computed tomographic colonography", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150Z (23 February 2012); https://doi.org/10.1117/12.911850
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