Automatic anatomical labeling of arteries and veins using conditional random fields
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For safe and reliable laparoscopic surgery, it is important to determine individual differences of blood vessels such as the position, shape, and branching structures. Consequently, a computer-assisted laparoscopy that displays blood vessel structures with anatomical labels would be extremely beneficial. This paper details an automated anatomical labeling method for abdominal arteries and veins extracted from 3D CT volumes.
The proposed method represents a blood vessel tree as a probabilistic graphical model by conditional random fields (CRFs). An adaptive gradient algorithm is adopted for structure learning. The anatomical labeling of blood vessel branches is performed by maximum a posteriori estimation.
We applied the proposed method to 50 cases of arterial and portal phase abdominal X-ray CT volumes. The experimental results showed that the F-measure of the proposed method for abdominal arteries and veins was 94.4 and 86.9%, respectively.
We developed an automated anatomical labeling method to annotate each blood vessel branches of abdominal arteries and veins using CRF. The proposed method outperformed a state-of-the-art method.
KeywordsAnatomical labeling Structure learning Probabilistic graphical model Abdomen
The authors thank colleagues for suggestions and advice.
Compliance with ethical standards
This work was supported in part by a Grant-In-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan (26560255, 26108006, 25242047, 15K01344), the Japan Society for the Promotion of Science, and the Practical Research for Innovative Cancer Control from Japan Agency for Medical Research and Development (AMED).
Conflict of interest
The authors declare that they have no conflict of interest.
The study was approved by the institutional review board of the Aichi Cancer Center.
Informed consent was obtained from all individual participants included in the study.
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