Carver: Learning to Reconstruct Right Ventricle from Sparse Multi-View 2D Echocardiograms

Abstract

Accurate 3D reconstruction of the right ventricle from multi-view echocardiograms is crucial for the quantitative diagnosis of cardiac diseases. However, existing methods often fail to deliver satisfactory results due to the structural complexity of the right ventricle and the sparsity of non-parallel ultrasound views. In this paper, we propose an efficient reconstruction method named Carver, which redefines 3D reconstruction of the right ventricle as a voxel-wise dense prediction task for the first time. The core idea lies in using a deep neural network to learn the end-to-end deformation from a coarse geometric convex hull to a fine structure of the right ventricle, similar to carving. To improve the reconstruction accuracy and robustness, we design a dual-aware network incorporating prior contour information to enhance learning representation. We conduct extensive experiments on an echocardiography dataset containing 1,278 instances to validate the effectiveness of the proposed method. Experimental results demonstrate that Carver outperforms existing state-of-the-art methods, achieving a Volume Similarity (VS) of 98.75%, a Dice Similarity Coefficient (DSC) of 97.80%, a Hausdorff Distance (HD) of 4.96, and a Root Mean Square Error (RMSE) of 0.013 for the ejection fraction, while maintaining considerable robustness even with sparser inputs. The code is available at https://github.com/ustclyd/Carver.