Jlk Inc.

Yonsei University

Jin Seong Jang

Myung Jae Lee

Shin Uk Kang

Dong Min Kim

Won Tae Kim

Do Sik Hwang

A cerebrovascular disease learning apparatus may be provided. The cerebrovascular disease learning apparatus may include a maximum intensity projection magnetic resonance angiography (MIP MRA) configuration unit configured to receive 3D time-of-flight magnetic resonance angiography (3D TOF MRA) and to construct MIP MRA including a plurality of image frames, a space characteristic learning unit configured to construct a space characteristic learning model, a frame characteristic learning unit configured to receive the space characteristics and to construct a frame characteristic learning model based on a recurrent neural network (RNN) learning method, and a lesion characteristic learning unit configured to receive the frame characteristics and to construct a lesion characteristic learning model based on the CNN learning method.

Apparatus for learning cerebrovascular disease, apparatus for detecting cerebrovascular disease, mehtod of learning cerebrovascular disease and method of detecting cerebrovascular disease

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

A method and apparatus for envelope detection are disclosed. An envelope detection method according to an embodiment of the invention can include: receiving ultrasonic signals reflected off a target object; detecting peaks by using differences in pulses in the received signals; and generating an envelope by connecting the detected peaks with straight or curved lines.

Growing community of inventors

Seoul, South Korea

Jin Seong Jang