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Using catheter ablation to treat atrial fibrillation increasingly relies onintracardiac echocardiography (ICE) for an anatomical delineation of the leftatrium and the pulmonary veins that enter the atrium. However, it is achallenge to build an automatic contouring algorithm because ICE is noisy andprovides only a limited 2D view of the 3D anatomy. This work provides the firstautomatic solution to segment the left atrium and the pulmonary veins from ICE.In this solution, we demonstrate the benefit of building a cross-modalityframework that can leverage a database of diagnostic images to supplement theless available interventional images. To this end, we develop a novel deepneural network approach that uses the (i) 3D geometrical information providedby a position sensor embedded in the ICE catheter and the (ii) 3D imageappearance information from a set of computed tomography cardiac volumes. Weevaluate the proposed approach over 11,000 ICE images collected from 150clinical patients. Experimental results show that our model is significantlybetter than a direct 2D image-to-image deep neural network segmentation,especially for less-observed structures.

More Knowledge is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring