The Asan Foundation

University of Ulsan Foundation for Industry Cooperation

Coreline Soft Co., Ltd.

Korea Advanced Institute of Science and Technology

Univ. of Ulsan Foundation for Industry Cooperation

Dong Hyun Yang

June Goo Lee

Hyun Woo Koo

Sang Min Lee

Jaeyoun Yi

Donghoon Yu

Jin Kook Kim

Young-Hak Kim

Seung Lee Park

JongChul Ye

Jung-Min Ahn

Joon Bum Seo

Eunhee Kang

Gaeun Lee

Disclosed is an apparatus for assisting the reading of a medical image based on a medical artificial neural network, the apparatus including a computing system. An apparatus according to an embodiment of the present invention includes a computing system, and the computing system includes at least one processor. The at least one processor acquires or receives a first analysis result obtained through the inference of a first artificial neural network for a first medical image, generates a first visualization format, which is a representative visualization format of the first medical image, based on the first analysis result, and visualizes the first medical image and the first analysis result based on the first visualization format.

Apparatus and method for medical image reading assistant providing representative image based on medical use artificial neural network

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

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.

Method for determining annulus size of bicuspid valve

A method for processing an unmatched low-dose X-ray computed tomography (CT) image using a neural network and an apparatus therefor are provided. The method includes receiving a low-dose X-ray CT image and removing noise from the low-dose X-ray CT image using a unsupervised learning based neural network learned using unmatched data to reconstruct a routine-dose X-ray CT image corresponding to the low-dose X-ray CT image.

Method for processing unmatched low-dose X-ray computed tomography image using neural network and apparatus therefor

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Seoul, South Korea

Dong Hyun Yang