title: Automatic anatomical labeling of arteries and veins using conditional random fields.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.affiliation: School of Information Science, Aichi Institute of Technology, Yachikusa, Yakusa-cho, Toyota, 470-0392, Japan. kitasaka@aitech.ac.jp.
authors.forename: Takayuki
authors.identifier: http://orcid.org/0000-0003-1648-1999
authors.initials: T
authors.lastname: Kitasaka
authors.affiliation: Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
authors.forename: Mitsuru
authors.initials: M
authors.lastname: Kagajo
authors.affiliation: Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
authors.forename: Yukitaka
authors.initials: Y
authors.lastname: Nimura
authors.affiliation: Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
authors.forename: Yuichiro
authors.initials: Y
authors.lastname: Hayashi
authors.affiliation: Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
authors.forename: Masahiro
authors.initials: M
authors.lastname: Oda
authors.affiliation: Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan.
authors.forename: Kazunari
authors.initials: K
authors.lastname: Misawa
authors.affiliation: Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
authors.forename: Kensaku
authors.initials: K
authors.lastname: Mori
mesh_terms: D000005:Abdomen / Q000000981:diagnostic imaging; D000465:Algorithms; D001158:Arteries / Q000000981:diagnostic imaging*; D006801:Humans; D010535:Laparoscopy / Q000379:methods; D015233:Models, Statistical; D011860:Radiography, Abdominal; D014057:Tomography, X-Ray Computed / Q000379:methods; D014680:Veins / Q000000981:diagnostic imaging*
keywords: Abdomen; Anatomical labeling; Probabilistic graphical model; Structure learning