14 February 2012 Automated anatomical labeling method for abdominal arteries extracted from 3D abdominal CT images
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Abstract
This paper presents an automated anatomical labeling method of abdominal arteries. In abdominal surgery, understanding of blood vessel structure concerning with a target organ is very important. Branching pattern of blood vessels differs among individuals. It is required to develop a system that can assist understanding of a blood vessel structure and anatomical names of blood vessels of a patient. Previous anatomical labbeling methods for abdominal arteries deal with either of the upper or lower abdominal arteries. In this paper, we present an automated anatomical labeling method of both of the upper and lower abdominal arteries extracted from CT images. We obtain a tree structure of artery regions and calculate feature values for each branch. These feature values include the diameter, curvature, direction, and running vectors of a branch. Target arteries of this method are grouped based on branching conditions. The following processes are separately applied for each group. We compute candidate artery names by using classifiers that are trained to output artery names. A correction process of the candidate anatomical names based on the rule of majority is applied to determine final names. We applied the proposed method to 23 cases of 3D abdominal CT images. Experimental results showed that the proposed method is able to perform nomenclature of entire major abdominal arteries. The recall and the precision rates of labeling are 79.01% and 80.41%, respectively.
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Masahiro Oda, Masahiro Oda, Bui Huy Hoang, Bui Huy Hoang, Takayuki Kitasaka, Takayuki Kitasaka, Kazunari Misawa, Kazunari Misawa, Michitaka Fujiwara, Michitaka Fujiwara, Kensaku Mori, Kensaku Mori, } "Automated anatomical labeling method for abdominal arteries extracted from 3D abdominal CT images", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142F (14 February 2012); doi: 10.1117/12.911685; https://doi.org/10.1117/12.911685
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