Jlk Inc.

Jlk Inspection

Dongguk University Industry-academic Cooperation Foundation

Shin Uk Kang

Myung Jae Lee

Won Tae Kim

Dong Min Kim

Jin Seong Jang

Dong-min Kim

Dong Eog Kim

Jisoo Son

Tae Won Kim

Dongmin Kim

Dong-Eog Kim

Jonghwan Back

An apparatus for providing brain lesion information based on an image includes a magnetic resonance angiography (MRA) provider configured to provide an environment capable of displaying 3D time-of-flight magnetic resonance angiography (3D TOF MRA) using user input, a brain lesion input unit configured to generate and manage a brain lesion image, a maximum intensity projection (MIP) converter configured to configure MIP image data including at least one image frame corresponding to a projection position of the brain lesion image, a noise remover configured to remove noise of brain lesion information and to configure corrected MIP image data, from which the noise is removed, and an MRA reconfiguration unit configured to reconfigure a corrected brain lesion image by back-projecting the corrected MIP image data.

Brain lesion information provision device and method

An apparatus for evaluating validity of detection of a cancer region may be provided. The apparatus comprises a parametric magnetic resonance imaging (MRI) provider configured to provide at least one MRI constructed based on different parameters, a first cancer region input unit configured to receive a first cancer region based on the at least one parametric MRI, a cancer region processor including a cancer region detection model for receiving the at least one parametric MRI as input and outputting cancer region information and configured to generate and provide guide information corresponding to an image to be analyzed through the cancer region detection model, a second cancer region input unit configured to receive a second cancer region based on the guide information, and a validity evaluator configured to generate validity evaluation information of the second cancer region, by comparing the first cancer region with the second cancer region based on a pathology image obtained by mapping a region, in which cancer is present, of an extracted body portion.

Validity evaluation device for cancer region detection

A deep learning model learning device is proposed, including: a parametric MRI image input part inputting an image corresponding to a diagnosis region, inputting at least one parametric MRI image constructed on the basis of parameters different from each other, and constructing and providing an MRI moving image by using the at least one parametric MRI image; a cancer detection model learning part receiving an input of the at least one parametric MRI image and the MRI moving image corresponding to the diagnosis region, and learning a deep learning model on the basis of information labeling the cancer region; a labeling reference information providing part providing at least one reference information contributing to the labeling of the cancer region; and a labeling processing part checking the cancer region input on the basis of the at least one reference information and processing the labeling of the checked cancer region.

Deep learning model learning device and method for cancer region

An apparatus for image analysis includes: an image acquisition unit for stacking a plurality of two-dimensional image data in a predetermined order; a three-dimensional image generation unit for generating a plurality of three-dimensional data on the basis of different types of multiple items of information for the plurality of two-dimensional image data in a stacked form from the image acquisition unit; and a deep learning algorithm analysis unit for applying a two-dimensional convolutional neural network to each of the plurality of three-dimensional data from the three-dimensional image generation unit, and combining results of applying the two-dimensional convolutional neural network to the plurality of three-dimensional data.

Apparatus and method for image analysis using virtual three-dimensional deep neural network

A stroke diagnosis and prognosis prediction system includes: an image acquisition unit configured so as to receive a plurality of images including at least a part of a human brain; an image alignment unit for aligning the plurality of images on the basis of a standard brain image; a lesion area detection and mapping unit for respectively detecting lesion areas from the plurality of images, and mapping the detected lesion areas so as to generate one mapping image; a matching and correction unit, which scales a mapping image so as to match the same to the standard brain image and performs image correction on the mapping image; a three-dimensional image generation unit storing the mapping image in a three-dimensional data space, thereby generating a three-dimensional lesion image; and a stroke diagnosis unit for diagnosing a stroke on the basis of the three-dimensional lesion image.

Stroke diagnosis and prognosis prediction method and system

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Seoul, South Korea

Shin Uk Kang