More Knowledge is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring
This provides the first automatic solution for ICE contouring, addressing a critical need in catheter ablation procedures for atrial fibrillation, though it is incremental in applying deep learning to a specific medical imaging domain.
The paper tackles the challenge of automatically segmenting the left atrium and pulmonary veins from noisy and limited 2D intracardiac echocardiography (ICE) images by developing a cross-modality framework that leverages 3D geometrical and appearance information from computed tomography. The model, evaluated on over 11,000 ICE images from 150 patients, significantly outperforms a direct 2D segmentation approach, particularly for less-observed structures.
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.