Paper Info
Title
More Knowledge Is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring.
Abstract
Using catheter ablation to treat atrial fibrillation increasingly relies on intracardiac echocardiography (ICE) for an anatomical delineation of the left atrium and the pulmonary veins that enter the atrium. However, it is a challenge to build an automatic contouring algorithm because ICE is noisy and provides only a limited 2D view of the 3D anatomy. This work provides the first automatic solution to segment the left atrium and the pulmonary veins from ICE. In this solution, we demonstrate the benefit of building a cross-modality framework that can leverage a database of diagnostic images to supplement the less available interventional images. To this end, we develop a novel deep neural network approach that uses the (i) 3D geometrical information provided by a position sensor embedded in the ICE catheter and the (ii) 3D image appearance information from a set of computed tomography cardiac volumes. We evaluate the proposed approach over 11,000 ICE images collected from 150 clinical patients. Experimental results show that our model is significantly better than a direct 2D image-to-image deep neural network segmentation, especially for less-observed structures.
Year
DOI
Venue
2018
10.1007/978-3-030-00934-2_60
Lecture Notes in Computer Science
DocType
Volume
ISSN
Journal
11071
0302-9743
Citations 
PageRank 
References 
0
0.34
5
Authors
5
Name
Order
Citations
PageRank
Haofu Liao1276.97
Yucheng Tang200.34
Gareth Funka-Lea3138363.84
Jiebo Luo46314374.00
Zhou S. Kevin547441.40