Coreline Soft Co., Ltd.

The Asan Foundation

Univ. of Ulsan Foundation for Industry Cooperation

University of Ulsan Foundation for Industry Cooperation

Jaeyoun Yi

Donghoon Yu

Jin Kook Kim

Hyun Gi Seo

Sang Min Lee

Yongjin Chang

Ji Min Kim

Byeong Soo Kim

Joon Beom Seo

Dong Hyun Yang

Seung Lee Park

Sunggoo Kwon

Hyunwoo Kim

Nam Kug Kim

Namkug Kim

Hyungi Seo

Seungbin Bae

Sol A Seo

Hyeon Ji

GyeongHyeon Bae

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

An apparatus includes a communication interface and at least one processor. The processor is configured to obtain a size of a lumen in each of a plurality of aortic zones and first information for each of the plurality of aortic zones based on segmentation results of an aorta and the plurality of aortic zones within the aorta; and visualize the size of the true lumen, the first information, and a ratio between the size of the true lumen and the first information in each of the plurality of aortic zones by using a visualization segment corresponding to each of the plurality of aortic zones.

Medical image visualization apparatus and method for diagnosis of aorta

Disclosed herein is an artificial neural network-based medical image analysis apparatus for analyzing a medical image based on a medical artificial neural network. The artificial neural network-based medical image analysis apparatus includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network from a first medical image, to input the first analysis result to a second artificial neural network, to acquire a first evaluation result obtained through the inference of the second artificial neural network from the first analysis result, and to provide the first evaluation result to a user as an evaluation result for the first medical image and the first analysis result.

Artificial neural network-based image analysis apparatus and method for evaluating analysis results of artificial neural network

Disclosed herein is an artificial neural network-based medical image diagnosis assistance apparatus for assisting in diagnosing a medical image based on a medical artificial neural network. A medical image diagnosis assistance 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 is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network about a first medical image, to detect user feedback on the first analysis result input by a user, to determine whether the first analysis result and the user feedback satisfy training conditions, and to transfer the first analysis result and the user feedback satisfying the training conditions to a style learning model so that the style learning model is trained on the first analysis result and the user feedback.

Medical image diagnosis assistance apparatus and method for providing user-preferred style based on medical artificial neural network

A medical image visualization apparatus includes a reception interface configured to receive at least one medical image; at least one processor; and a display. The at least one processor is configured to recognize a connection image that is presented on the at least one medical image and connects the at least one medical image and detailed analysis information regarding the at least one medical image; acquire the detailed analysis information, connected to the at least one medical image by the connection image; and visualize the detailed analysis information on the display. The detailed analysis information includes an intermediate result and a final result of a first process in which an image analysis result of an original medical image is generated as the at least one medical image.

Medical image analysis apparatus and method, and medical image visualization apparatus and method

Disclosed herein is a system for an interventional procedure using medical images. The system includes: a master device configured to perform control so that a medical tool of a robot arm follows intervention trajectories; an image processing device configured to register plan images, in which a surgical plan including the intervention trajectories of the medical tool is set up, and preoperative (pre-op) images acquired at a surgical site before surgery so that the surgical plan is transferred to the pre-op images and register the pre-op images and intraoperative (intra-op) images acquired at the surgical site during the surgery; and a user interface device configured to visualize the predicted arrival location of the tip of the medical tool for a target point when the robot arm operates according to the surgical plan by using the pre-op images and the intra-op images in conjunction with the image processing device.

System and method for interventional procedure using medical images

Disclosed herein is a medical image reconstruction apparatus for reconstructing a medical image to assist the reading of a medical image. The medical image reconstruction apparatus includes a computing system, which includes: a receiver interface configured to receive a first medical image to which a first reconstruction parameter adapted to diagnose or analyze a first type of lesion is applied; and at least one processor configured to generate a second reconstruction parameter to be applied to the first medical image in response to a diagnosis order for the diagnosis or analysis of a second type of lesion. The at least one processor provides the second reconfiguration parameter to a user via a user interface, or generates a second medical image for the diagnose or analysis of the second type of lesion by executing the second reconstruction parameter on the first medical image and provides the second medical image to the user.

Medical image reconstruction apparatus and method for screening for plurality of types of lung diseases

Disclosed herein is a computing system for performing medical image analysis. A computing system for performing medical image analysis according to an embodiment of the present invention includes at least one processor. The at least one processor performs image processing on a first medical image, and segments at least one anatomical region in the first medical image. The at least one processor generates a first quantitative parameter for the at least one anatomical region based on quantitative measurement conditions that can be measured in the first medical image, and stores the first quantitative parameter in a database in association with the first medical image and the at least one anatomical region.

Medical image analysis system and similar case retrieval system using quantitative parameters, and methods for the same

Disclosed herein is a medical image reading assistant apparatus that provides hanging protocols based on a medical artificial neural network. The medical image reading assistant apparatus includes a computing system, and the computing system includes at least one processor. The at least one processor is configured to acquire or receive a first analysis result obtained through the inference of a first artificial neural network from a first medical image, to generate a first display setting based on the first analysis result, and to execute the first display setting so that the first medical image and the first analysis result are displayed on a screen based on the first display setting.

Medical image reading assistant apparatus and method providing hanging protocols based on medical use artificial neural network

Disclosed herein is a medical image reading assistant apparatus. The medical image reading assistant apparatus includes a computing system. The computing system includes: a receiver interface configured to receive a first medical image and a second medical image following the first medical image; and at least one processor configured to: generate follow-up information between first findings on the first medical image and second findings on the second medical image by comparing the locations of the first findings and the locations of the second findings; select at least some of the first findings as first valid findings based on a first threshold of diagnostic assistant information for each of the first findings; set a second threshold of diagnostic assistant information for each of the second findings based on the follow-up information; and select at least some of the second findings as second valid findings based on the second threshold.

Medical image reading assistant apparatus and method for adjusting threshold of diagnostic assistant information based on follow-up examination

Medical use artificial neural network-based medical image analysis apparatus and method for evaluating analysis results of medical use artificial neural network

Disclosed is a method for measuring a representative value of a duct in vivo, the method including: selecting, by selection unit, at least one duct and sampling measurement sites in each duct; measuring, by creation unit, at least one real cross-section image being sampled to obtain measurement values and creating a measurement cross-section image; in comparing, by comparison-decision unit, the measurement cross-section image with the real cross-section image and evaluating validity of measurement values whether to accept the measurement values as data; and computing, by computation unit, a representative value out of the measurement values accepted as data.

Method and system for measuring representative value of duct in vivo

A local server system and a method of relaying data in the local server system are disclosed. The local server system includes a database configured to store image data downloaded from a remote server, and a processor configured to request the image data from the remote server and download the image data. The processor requests first image data corresponding to first result data from the remote server. The processor downloads the first image data from the remote server. The processor generates second result data based on the downloaded first image data. The processor provides the second result data to the local client when the local client requests the second result data corresponding to the first image data from the remote server.

Local server system and method of relaying data in the same for switching between thin client environment and thick client environment

Disclosed are a computing system and method for displaying medical images. The computing system includes a display, and a processor configured to control image information displayed on the display. The processor includes: a receiving unit configured to receive a medical image of a region of interest (ROI) and to acquire three-dimensional (3D) volume information including segmentation information regarding the ROI of the medical image; a display information generation unit configured to acquire 3D mesh information corresponding to the ROI, and to generate display information in which the 3D mesh information has been overlaid on the 3D volume information in a 3D space including the 3D volume information; and a user interface control unit configured to provide a user menu so that a user can edit the 3D mesh information in the 3D space.

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Seoul, South Korea

Jaeyoun Yi