  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.
