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

arXiv: https://arxiv.org/abs/1812.03507 · HTML: https://mlsanity.com/paper/1812.03507/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [AI-driven View Guidance System in Intra-cardiac Echocardiography Imaging](https://mlsanity.com/paper/2409.16898/) (2024)
- [From Sparse to Precise: A Practical Editing Approach for Intracardiac Echocardiography Segmentation](https://mlsanity.com/paper/2303.11041/) (2023)
- [Neural Network Reconstruction of the Left Atrium using Sparse Catheter Paths](https://mlsanity.com/paper/2311.02488/) (2023)

## Abstract (arXiv)

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.