PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data

TL;DR

This paper introduces PC-U Net, a joint learning framework that reconstructs 3D cardiac wall shapes and segments them directly from CT data, improving accuracy over traditional methods by incorporating shape priors.

Contribution

The novel PC-U Net simultaneously reconstructs 3D cardiac shapes and segments myocardium from CT slices, outperforming existing methods by integrating shape priors into the segmentation process.

Findings

Improved segmentation accuracy with higher Dice's coefficient.

More precise shape modeling with reduced errors in Hausdorff distance.

Joint reconstruction and segmentation outperform separate methods.

Abstract

The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation. Many cardiac image segmentation methods have relied on detection of region-of-interest as a pre-requisite for shape segmentation and modeling. With segmentation results, a 3D surface mesh and a corresponding point cloud of the segmented cardiac volume can be reconstructed for further analyses. Although state-of-the-art methods (e.g., U-Net) have achieved decent performance on cardiac image segmentation in terms of accuracy, these segmentation results can still suffer from imaging artifacts and noise, which will lead to inaccurate shape modeling results. In this paper, we propose a PC-U net that jointly reconstructs the point cloud of the LV MYO wall directly from volumes of 2D CT slices and generates its segmentation masks from the predicted 3D point cloud. Extensive experimental results show that by incorporating a shape prior from the point cloud, the segmentation masks are more accurate than the state-of-the-art U-Net results in terms of Dice's coefficient and Hausdorff distance.The proposed joint learning framework of our PC-U net is beneficial for automatic cardiac image analysis tasks because it can obtain simultaneously the 3D shape and segmentation of the LV MYO walls.