PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data
This work addresses cardiac disease diagnosis and procedure navigation by providing more accurate 3D shape and segmentation for the left ventricle myocardium, though it is incremental as it builds on existing U-Net methods.
The paper tackles the problem of inaccurate 3D shape modeling from cardiac CT segmentation due to imaging artifacts by proposing PC-U Net, which jointly reconstructs a 3D point cloud and segmentation masks, achieving improved accuracy over U-Net with higher Dice's coefficient and lower Hausdorff distance.
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.