University of Ulsan Foundation for Industry Cooperation

The Asan Foundation

Ln Robotics Inc.

June Goo Lee

Dong Hyun Yang

Young-Hak Kim

Hyun Woo Koo

Young Jin Moon

Jihoon Kweon

Young Hak Kim

Soo Jin Kang

Hyung Joo Cho

Jung-Min Ahn

Gaeun Lee

Jaesoon Choi

The present invention relates to an image processing system for a catheter operation. One embodiment comprises: a storage unit for at least temporarily storing a plurality of candidate routes on which a guide wire can proceed on a changing route caused by vascular bifurcations while proceeding from a first point by using a blood vessel image; and a processor for selecting, from an image inputted while an operation proceeds, at least one route corresponding to the current shape of the guide wire on the basis of a similarity comparison between the current shape of the guide wire and each of the plurality of candidate routes.

Apparatus and method for evaluating proceeding route of guide wire

A myocardium image analysis method comprising acquiring a target image including precontrast-enhanced myocardium, based on a type of coronary artery related to the myocardium, distinguishing the myocardium included in the target image, using an artificial neural network, providing information on the distinguished myocardium, wherein the artificial neural network is trained based on a training database generated by matching images for training of precontrast-enhanced coronary artery and myocardium and images for training of postcontrast-enhanced coronary artery and myocardium.

Myocardium image analysis method and device

A method for calculating a coronary artery calcium score, the method comprising acquiring a target image for a coronary artery and myocardium before contrast enhancement, identifying the coronary artery included in the target image by using an artificial neural network, calculating a coronary artery calcium score based on the identified coronary artery, wherein the artificial neural network is trained based on a training database generated via alignment between a pre-acquired image of a coronary artery and myocardium before contrast enhancement and a pre-acquired image of a coronary artery and myocardium after contrast enhancement.

Method and apparatus for calculating coronary artery calcium score

A medical image learning method of a medical image process apparatus includes preparing a plurality of body X-ray images for learning as an input of a learning data set, preparing internal diagnostic indicator information corresponding to each of the plurality of body X-ray images for learning as a label of the learning data set, and learning an artificial neural network model using the learning data set. The internal diagnostic indicator information includes information on a cardiovascular border. The information on the cardiovascular border includes information on at least one of an aortic knob, a pulmonary conus, a left atrial appendage, an upper right cardiac border, a lower right cardiac border, a lower left cardiac border, a descending aorta, a carina, an upper end point of a diaphragm, a right pulmonary artery, a posterior cardiac border, and an anterior spinal border.

Medical image process apparatus, medical image learning method, and medical image process method

Provided are a diagnostic device and a diagnostic method for predicting fractional flow reserve (FFR) and diagnosing coronary artery lesions via a coronary angiography-based machine learning algorithm. A deep learning-based diagnostic method for diagnosing an ischemic lesion includes: obtaining an angiography image of a patient's blood vessel; extracting a region of interest (ROI) from the angiography image; acquiring diameter information of the blood vessel in the ROI; extracting morphological features of the blood vessel based on the diameter information; and obtaining a predictive FFR value by inputting the morphological features to an artificial intelligence (AI) model and determining whether a lesion is an ischemic lesion.

Diagnostic device, diagnostic method and recording medium for diagnosing coronary artery lesions through coronary angiography-based machine learning

A method for determining the size of a bicuspid annulus of bicuspid, including acquiring an image of the heart including the left ventricle and the aorta; generating a first plane, which includes a line that passes through two base points in the bicuspid of the image of the heart; and generating multiple second planes, which are obtained per each rotation, while rotating the first plane multiple times by a predetermined angle about the line that passes through the two base points; measuring the cross-sectional area of each of at least one of the left ventricle and the aorta, which are formed on the first plane and the multiple second planes; selecting a plane for measuring the size of a bicuspid annulus among the first plane and the multiple second planes based on the measured cross-sectional area; and measuring the size of the bicuspid annulus based on the selected plane.

Growing community of inventors

Seoul, South Korea

June Goo Lee