Automatic anatomical labeling of arteries and veins using conditional random fields

  • Takayuki Kitasaka
  • Mitsuru Kagajo
  • Yukitaka Nimura
  • Yuichiro Hayashi
  • Masahiro Oda
  • Kazunari Misawa
  • Kensaku Mori
Original Article

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.

Keywords

Anatomical labeling Structure learning Probabilistic graphical model Abdomen 

Notes

Acknowledgements

The authors thank colleagues for suggestions and advice.

Compliance with ethical standards

Funding

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.

Ethical approval

The study was approved by the institutional review board of the Aichi Cancer Center.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© CARS 2017

Authors and Affiliations

  1. 1.School of Information ScienceAichi Institute of TechnologyYachikusa, Yakusa-cho, ToyotaJapan
  2. 2.Graduate School of Information ScienceNagoya UniversityFuro-cho, Chikusa-ku, NagoyaJapan
  3. 3.Information and CommunicationsNagoya UniversityFuro-cho, Chikusa-ku, NagoyaJapan
  4. 4.Department of Gastroenterological SurgeryAichi Cancer Center HospitalKanokoden, Chikusa-ku, NagoyaJapan

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