Autonomous segmentation of contrast filled coronary artery vessels on computed tomography images

A computer-implemented method of generating a patient-specific three-dimensional surface mesh of contrast-filled coronary-artery vessels, the method comprising:

• (a) receiving a computed-tomography (CT) scan volume representing a three-dimensional region that includes a pericardium;
• (b) pre-processing the CT scan volume to obtain a normalized CT volume;
• (c) computing a three-dimensional Jerman filter response of the normalized CT volume to obtain a vesselness volume;
• (d) forming a multi-channel volume whose first channel is the normalized CT volume and whose second channel is the vesselness volume;
• (e) segmenting the pericardium by applying a first convolutional neural network to a plurality of three-dimensional sub-volumes of the multi-channel volume and combining resulting per-sub-volume predictions to generate a whole-volume pericardium mask;
• (f) dilating the pericardium mask with a spherical structuring element to create a safety-margin mask;
• (g) masking both channels of the multi-channel volume with the safety-margin mask to obtain a masked multi-channel volume;
• (h) dividing the masked multi-channel volume into three-dimensional sub-volumes and, for each sub-volume, applying a second convolutional neural network that concurrently receives the two channels and outputs a coronary-vessel probability map, and combining the probability maps of all sub-volumes to obtain a whole-volume binary coronary-vessel mask;
• (i) post-processing the binary coronary-vessel mask by removing connected components having fewer than a threshold number of voxels and applying morphological smoothing;
• and
• (j) converting the post-processed coronary-vessel mask to a triangulated surface mesh that represents the patient's contrast-filled coronary-artery vessels.