Kardiolytics Inc.

Holo Surgical Inc.

Inteneural Networks Inc.

Augmedics Ltd.

Marek Kraft

Kris Siemionow

Dominik Pieczynski

Paul Lewicki

Jacek Kania

Zbigniew Malota

Wojciech Sadowski

Michal Mikolajczak

Cristian J Luciano

Mikolaj Pawlak

Edwing Isaac Mejia Orozco

Krzysztof B Siemionow

Dominik Gawel

Michal Klimont

Michal Fularz

Michal Trzmiel

Krzystof B Siemionow

Mikolaj Andrzej Pawlak

Kris B Siemionow

Isaac Mejia Orozco Edwing

Michal Pietruszka

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

A method for generating an intraoperative 3D brain model while a patient is operated. Before an opening in a patient's skull is made, the method includes: providing a preoperative 3D brain model of a patient's brain and converting it to a preoperative 3D brain point cloud; providing a preoperative 3D face model of a patient's face and converting it to a preoperative 3D face point cloud. After the opening in the patient's skull is made, the method includes: matching the intraoperative 3D face point cloud with the preoperative 3D face point cloud to find a face point transformation; transforming the intraoperative 3D brain point cloud based on said face point cloud transformation; comparing the intraoperative 3D brain point cloud with the preoperative 3D brain point cloud to determine a brain shift; and converting the preoperative 3D brain model to generate an intraoperative 3D brain model based on said brain shift.

Method and apparatus for registering a neurosurgical patient and determining brain shift during surgery using machine learning and stereooptical three-dimensional depth camera with a surface-mapping system

A computer-implemented method for autonomous reconstruction of vessels on computed tomography images, includes: providing a reconstruction convolutional neural network (CNN); receiving an input 3D model of a vessel to be reconstructed; defining a region of interest (ROI) and a movement step, wherein the ROI is a 3D volume that covers an area to be processed; defining a starting position and positioning the ROI at the starting position; reconstructing a shape of the input 3D model within the ROI by inputting the fragment of the input 3D model within the ROI to the reconstruction convolutional neural network (CNN) and receiving the reconstructed 3D model fragment; moving the ROI by the movement step along a scanning path; repeating the reconstruction and moving steps to reconstruct a desired portion of the input 3D model at consecutive ROI positions; and combining the reconstructed 3D model fragments.

Autonomous reconstruction of vessels on computed tomography images

A method for determining a brain age, the method comprising the following: providing a brain age determining convolutional neural network (CNN) (); training the CNN () to determine the brain age based on a plurality of sets of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least two types of MRI volumes, wherein the at least one type of brain tissue on the first type of the MRI volume is represented by a different contrast with respect to other tissues than on a second type of the MRI volume; and performing an inference process using the trained CNN () to determine the brain age based on the set of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least the two types of the MRI volumes as used for the training.

System and a method for determining brain age using a neural network

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.

A method for computer assisted identification of appropriate anatomical structure for placement of a medical device, comprising: receiving a 3D scan volume comprising set of medical scan images of a region of an anatomical structure where the medical device is to be placed; automatically processing the set of medical scan images to perform automatic segmentation of the anatomical structure; automatically determining a subsection of the 3D scan volume as a 3D ROI by combining the raw medical scan images and the obtained segmentation data; automatically processing the ROI to determine the preferred 3D position and orientation of the medical device to be placed with respect to the anatomical structure by identifying landmarks within the anatomical structure with a pre-trained prediction neural network; automatically determining the preferred 3D position and orientation of the medical device to be placed with respect to the 3D scan volume of the anatomical structure.

Computer assisted identification of appropriate anatomical structure for medical device placement during a surgical procedure

A computer-implemented system: at least one processor communicably coupled to at least one nontransitory processor-readable storage medium storing processor-executable instructions or data receives segmentation learning data comprising a plurality of batches of labeled anatomical image sets, each image set comprising image data representative of a series of slices of a three-dimensional bony structure, and each image set including at least one label which identifies the region of a particular part of the bony structure depicted in each image of the image set, wherein the label indicates one of a plurality of classes indicating parts of the bone anatomy; trains a segmentation CNN, that is a fully convolutional neural network model with layer skip connections, to segment semantically at least one part of the bony structure utilizing the received segmentation learning data; and stores the trained segmentation CNN in at least one nontransitory processor-readable storage medium of the machine learning system.

Automated segmentation of three dimensional bony structure images

Growing community of inventors

Poznan, Poland

Marek Kraft