Kardiolytics Inc.

Zbigniew Malota

Wojciech Sadowski

Marek Kraft

Kris Siemionow

Paul Lewicki

Dominik Pieczynski

Jacek Kania

A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels includes 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; dividing the CT scan volume into a first set of subvolumes; extracting a region of interest by autonomous segmentation of the heart region as outlined by the pericardium, by means of a neural network trained on 3D subvolumes and combining the results of the individual subvolume predictions for the first set 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 a second set of 3D subvolumes; and performing autonomous coronary vessel segmentation to output a mask denoting the coronary vessels.

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

Method for modelling blood vessels includes: obtaining medical imaging data of the blood vessels; generating a three-dimensional personalized model of the blood vessels, based on the medical imaging data; generating a three-dimensional reference model of the blood vessels that reflects a state of healthy blood vessels that lack lesions, based on the medical imaging data or based on numerical reconstruction of the personalized model; performing a numerical simulation of blood flow for the same physical and boundary conditions in the personalized model and in the reference model, the simulation comprising determining conditions of blood flow at an inlet to the blood vessels model and calculating blood flow energy for the inlet and all outlets of the blood vessels model; comparing the blood flow energy measured for the personalized model and for the reference model; determining flow energy change indexes of the blood flow in the personalized model and the reference model.

Modelling blood vessels and blood flow

A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels, the method comprising the following steps: receiving () an x-ray angiography scan representing a maximum intensity projection of a region of anatomy that includes the coronary vessels on the imaging plane; preprocessing () the scan to output a preprocessed scan; and performing autonomous coronary vessel segmentation () by means of a trained convolutional neural network (CNN) that is trained to process the preprocessed scan data to output a mask denoting the coronary vessels.

Method and system for machine learning based segmentation of contrast filled coronary artery vessels on medical images

A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels includes 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; 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; and performing autonomous coronary vessel segmentation to output a mask denoting the coronary vessels.

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Zabrze, Poland

Zbigniew Malota