Abstract: A method of anonymizing medical images includes acquiring or receiving a first medical image; separating an identification area containing anatomical structure information and a de-identification area containing a skin area in the first medical image; generating a de-identified image including a plurality of pattern areas with a predetermined size for the de-identification area; and generating a second medical image by replacing the de-identification area with the de-identified image.

Abstract: 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.

Abstract: A method according to an embodiment of the present disclosure includes obtaining a first branch region of an anatomical tubular structure in a first medical image and quantitative information of the first branch region; identifying a second branch region matching the first branch region of the anatomical tubular structure in a second medical image obtained after the first medical image; obtaining quantitative information of the second branch region; and generating diagnostic assistant information based on the quantitative information of the first branch region and the quantitative information of the second branch region.

Abstract: A method according to an embodiment of the present disclosure includes obtaining a first finding region in a first medical image and size information of the first finding region; identifying a second finding region matching the first finding region in a second medical image obtained after the first medical image; obtaining size information of the second finding region; and generating diagnosis assistance information based on the size information of the first finding region and the second finding region.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed are a visualization method for assisting medical image diagnosis comprising: acquiring first intensity values of first voxels in a lung region during inspiration, segmented from a chest computed tomography (CT) image acquired during inspiration, as first coordinate values of the first voxels; acquiring differences between second intensity values of second voxels, registered into the first voxels as voxels in the lung region during expiration segmented from a chest CT image acquired during expiration, and the first intensity values as second coordinate values of the first voxels; and visualizing a distribution of the first voxels by mapping the first voxels based on the first coordinate values and the second coordinate values.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed therein are a medical image reading assistance apparatus and method for assisting the reading of chest medical images. The medical image reading assistance apparatus includes a computing system, and the computing system includes at least one processor. The at least one processor segments airways and blood vessels from a medical image including the lungs, segments small regions corresponding to a plurality of intensity sections divided and set based on at least one of the property and state of tissue in a lung area image generated by excluding the airways and the blood vessels from the medical image, and visualizes the distributions of the small regions corresponding to the plurality of intensity sections within the lung area image.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.