  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.
