Automated nomenclature of upper abdominal arteries for displaying anatomical names on virtual laparoscopic images

Authors:
Kensaku Mori;Masahiro Oda;Tomohiko Egusa;Zhengang Jiang;Takayuki Kitasaka;Michitaka Fujiwara;Kazunari Misawa
Affiliations:
Information and Communications Headquarters, Nagoya University, Japan and Graduate School of Information Science, Nagoya University, Japan;Information and Communications Headquarters, Nagoya University, Japan;Graduate School of Information Science, Nagoya University, Japan;Graduate School of Information Science, Nagoya University, Japan;Faculty of Information Science, Aichi Institute of Technology, Japan;Graduate School of Medicine, Nagoya University, Japan;Aichi Cancer Center Hospital, Japan
Venue:
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
Year:
2010

Citing 1
Cited 1

Automated Anatomical Labeling of Bronchial Branches Extracted from CT Datasets Based on Machine Learning and Combination Optimization and Its Application to Bronchoscope Guidance

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II

Anatomical labeling of the anterior circulation of the circle of willis using maximum a posteriori classification

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method for automated nomenclature of abdominal arteries that are extracted from 3D CT images based on the combination optimization approach for the displaying anatomical names on virtual laparoscopic images. It is important to understand the blood vessel network of a patient. Our proposed method recognizes the anatomical names of each arterial branch extracted from contrasted 3D images based on geometric features. We employ a combination optimization approach for treating the variations of branching patterns and overlay recognized anatomical names on virtual laparoscopic views for assisting the recognition of patient anatomy for surgeons. Experimental results using 89 cases of 3D CT images showed that the nomenclature accuracy for uncorrected blood vessel tree and corrected blood vessel tree were about 84.2% and 88.8% in average respectively and demonstrated anatomical name overlay on virtual laparoscopic images.