Paper Info
Title
BRANCH: Bifurcation Recognition for Airway Navigation based on struCtural cHaracteristics.
Abstract
Bronchoscopic navigation is challenging, especially at the level of peripheral airways due to the complicated bronchial structures and the large respiratory motion. The aim of this paper is to propose a localisation approach tailored for navigation in the distal airway branches. Salient regions are detected on the depth maps of video images and CT virtual projections to extract anatomically meaningful areas that represent airway bifurcations. An airway descriptor based on shape context is introduced which encodes both the structural characteristics of the bifurcations and their spatial distribution. The bronchoscopic camera is localised in the airways by minimising the cost of matching the region features in video images to the pre-computed CT depth maps considering both the shape and temporal information. The method has been validated on phantom and in vivo data and the results verify its robustness to tissue deformation and good performance in distal airways.
Year
Venue
Field
2017
MICCAI
Computer vision,Pattern recognition,Respiratory motion,Computer science,Imaging phantom,Robustness (computer science),Artificial intelligence,Airway,Shape context,Tissue deformation,Bifurcation
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
Mali Shen183.70
Stamatia Giannarou219218.86
Pallav Shah311.40
Guang-Zhong Yang42812297.66