A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels, the method comprising the following steps: - receiving a CT scan volume representing a 3D volume of a region of anatomy that includes a pericardium; - preprocessing the CT scan volume to output a preprocessed scan volume; - converting the CT scan volume to three sets of two-dimensional slices, wherein the first set is arranged along the axial plane, the second set is arranged along the sagittal plane and the third set is arranged along the coronal plane; - extracting a region of interest, ROI, by autonomous segmentation of the heart region as outlined by the pericardium, by means of three individually trained ROI extraction convolutional neural networks, CNN, each trained to process a particular one of the three sets of two-dimensional slices to output a mask denoting a heart region as delineated by the pericardium; - combining the preprocessed scan volume with the mask to obtain a masked volume; - converting the masked volume to three groups of sets of two-dimensional masked slices, wherein the first group corresponds to the axial plane, the second group corresponds to the sagittal plane and the third group corresponds to the coronal plane and each group includes at least three sets, wherein the first set corresponds to the principal plane of the set and at least two other sets are tilted at different angles with respect to the principal plane of the set; and - performing autonomous coronary vessel segmentation by autonomous segmentation of the sets of the two-dimensional masked slices by means of three individually trained segmentation convolutional neural networks, CNN, each trained to process a particular one of the sets of the two-dimensional masked slices to output a mask denoting the coronary vessels. - receiving a CT scan volume representing a 3D volume of a region of anatomy that includes a pericardium; - preprocessing the CT scan volume to output a preprocessed scan volume; - converting the CT scan volume to three sets of two-dimensional slices, wherein the first set is arranged along the axial plane, the second set is arranged along the sagittal plane and the third set is arranged along the coronal plane; - extracting a region of interest, ROI, by autonomous segmentation of the heart region as outlined by the pericardium, by means of three individually trained ROI extraction convolutional neural networks, CNN, each trained to process a particular one of the three sets of two-dimensional slices to output a mask denoting a heart region as delineated by the pericardium; - combining the preprocessed scan volume with the mask to obtain a masked volume; - converting the masked volume to three groups of sets of two-dimensional masked slices, wherein the first group corresponds to the axial plane, the second group corresponds to the sagittal plane and the third group corresponds to the coronal plane and each group includes at least three sets, wherein the first set corresponds to the principal plane of the set and at least two other sets are tilted at different angles with respect to the principal plane of the set; and - performing autonomous coronary vessel segmentation by autonomous segmentation of the sets of the two-dimensional masked slices by means of three individually trained segmentation convolutional neural networks, CNN, each trained to process a particular one of the sets of the two-dimensional masked slices to output a mask denoting the coronary vessels. <EOS>