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 are a method of determining branching levels of a plurality of airway branch segment regions of an airway region, the method comprising: determining branching levels of a plurality of first airway branch segment regions of an airway region included in a medical image from a root to a first order based on anatomical structure information; and determining branching levels of a plurality of second airway branch segment regions of the airway region from a bottom level to the first order.

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 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 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: 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 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 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 are a system and method for dental implant planning. The method for dental implant planning includes: detecting a region in which a plurality of teeth has been lost based on a teeth image; placing a plurality of virtual crowns in the region in which the plurality of teeth has been lost; receiving a user's confirmation for the plurality of virtual crowns; and setting up a plan for a plurality of implant objects, to be placed in the region in which the plurality of teeth has been lost, based on the plurality of virtual crowns.

Abstract: Disclosed is a method for the quantification of pulmonary vessels by lobe, the method including extracting, at extraction unit, pulmonary vessels based on a medical image, locating, at analysis unit, voxels of pulmonary vessels with respect to the surface of a lobe, and quantifying, at calculation unit, the extracted pulmonary vessels.

Abstract: 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.

Abstract: Disclosed herein are a computer assistance method for the reduction of a fracture and a computing system for assisting the reduction of a fracture. The computer assistance method includes: generating a first model of a first bone, which is a treatment target, based on a pre-operative medical image; generating a second model, assumed to be obtained after the restoration of the first bone, based on the pre-operative medical image; acquiring a third model in which the spatial information of the first model has been updated by incorporating, into the spatial information, the movement of the first bone occurring during treatment after the installation of markers onto the first bone; and generating overlay visual information based on the second and third models registered onto the same space.

Abstract: 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.

Abstract: 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.

Abstract: A processor and method for accelerating ray casting are disclosed herein. The processor for accelerating ray casting includes a computation unit, a sorting unit, an allocation unit, and an execution control unit. The computation unit calculates the length information of a section in which a ray corresponding to each of the pixels of a two-dimensional (2D) scene corresponding to a viewpoint intersects an effective volume in order to apply ray casting to the pixel. The sorting unit sorts the ray based on the length information of the section in which the ray intersects the effective volume. The allocation unit allocates the sorted rays to respective thread groups having a parallel multiprocessor structure in order of the sorting. The execution control unit transfers control instructions to the allocated thread groups so that the allocated thread groups execute ray casting for the sorted rays.