A method for automated anatomical labeling of abdominal veins extracted from 3D CT images

Tetsuro Matsuzaki; Masahiro Oda; Takayuki Kitasaka; Yuichiro Hayashi; Kazunaru Misawa; Kensaku Mori

doi:10.1117/12.2006745

13 March 2013 A method for automated anatomical labeling of abdominal veins extracted from 3D CT images

Tetsuro Matsuzaki, Masahiro Oda, Takayuki Kitasaka, Yuichiro Hayashi, Kazunaru Misawa, Kensaku Mori

Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86691Y (2013) https://doi.org/10.1117/12.2006745
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

In abdominal surgery, understanding blood vessel structure is important because abdominal blood vessels have large individual differences among patients. Computers must be used to support surgeons and their understanding of blood vessel structures. This paper presents a method of automated anatomical labeling of abdominal veins. A thinning process is applied to the abdominal vein regions extracted from a CT volume. The result of the process is expressed as a tree structure. Since portal veins have a characteristic shape and position in the portal system, we applied rule-based anatomical labeling to them. The names of other veins are assigned by classifiers trained by a machine learning technique, where several likelihood functions are constructed for each vessel name. Their weighted sum is used as the likelihood of the vessel name. The names of the branches in the tree structure are labeled by searching for the branch whose likelihood of an anatomical name is maximum and assigning the anatomical name to the branch. In an experiment using 50 cases of abdominal CT volumes, the recall rate, the precision rate, and the F-measure were 87.5, 93.1, and 90.2%, respectively.

Citation Download Citation

Tetsuro Matsuzaki, Masahiro Oda, Takayuki Kitasaka, Yuichiro Hayashi, Kazunaru Misawa, and Kensaku Mori "A method for automated anatomical labeling of abdominal veins extracted from 3D CT images", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691Y (13 March 2013); https://doi.org/10.1117/12.2006745

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