Segmentation and Vascular Vectorization for Coronary Artery by
  Geometry-based Cascaded Neural Network
Xiaoyu Yang Lijian Xu Simon Yu Qing Xia Hongsheng Li Shaoting Zhang
Segmentation of the coronary artery is an important task for the quantitative
analysis of coronary computed tomography angiography (CCTA) images and is being
stimulated by the field of deep learning. However, the complex structures with
tiny and narrow branches of the coronary artery bring it a great challenge.
Coupled with the medical image limitations of low resolution and poor contrast,
fragmentations of segmented vessels frequently occur in the prediction.
Therefore, a geometry-based cascaded segmentation method is proposed for the
coronary artery, which has the following innovations: 1) Integrating geometric
deformation networks, we design a cascaded network for segmenting the coronary
artery and vectorizing results. The generated meshes of the coronary artery are
continuous and accurate for twisted and sophisticated coronary artery
structures, without fragmentations. 2) Different from mesh annotations
generated by the traditional marching cube method from voxel-based labels, a
finer vectorized mesh of the coronary artery is reconstructed with the
regularized morphology. The novel mesh annotation benefits the geometry-based
segmentation network, avoiding bifurcation adhesion and point cloud dispersion
in intricate branches. 3) A dataset named CCA-200 is collected, consisting of
200 CCTA images with coronary artery disease. The ground truths of 200 cases
are coronary internal diameter annotations by professional radiologists.
Extensive experiments verify our method on our collected dataset CCA-200 and
public ASOCA dataset, with a Dice of 0.778 on CCA-200 and 0.895 on ASOCA,
showing superior results. Especially, our geometry-based model generates an
accurate, intact and smooth coronary artery, devoid of any fragmentations of
segmented vessels.