Takayuki Kitasaka1, Mitsuru Kagajo2, Yukitaka Nimura3, Yuichiro Hayashi3, Masahiro Oda2, Kazunari Misawa4, Kensaku Mori3. 1. School of Information Science, Aichi Institute of Technology, Yachikusa, Yakusa-cho, Toyota, 470-0392, Japan. kitasaka@aitech.ac.jp. 2. Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan. 3. Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan. 4. Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Authors: Hrvoje Bogunovic; José María Pozo; Rubén Cárdenes; Luis San Román; Alejandro F Frangi Journal: IEEE Trans Med Imaging Date: 2013-04-23 Impact factor: 10.048