公开(公告)号：US20190156524A1

公开(公告)日：2019-05-23

申请号：US16194941

申请日：2018-11-19

Applicant: ClariPI Inc. ,  Seoul National University R&DB Foundation

Inventor： Hyun Sook PARK ,  Jong Hyo KIM

IPC: G06T11/00 ,  G06T5/00 ,  G06T7/00

Abstract: Provided is a method for CT image denoising based on deep learning, and the method for CT image denoising based on deep learning includes: extracting examination information from an input CT image; selecting at least one deep learning model corresponding to the examination information from multiple previously trained deep learning models; and outputting a CT image denoised from the input CT image by feeding the input CT image into the selected at least one deep learning model.

公开(公告)号：US20250022261A1

公开(公告)日：2025-01-16

申请号：US18771443

申请日：2024-07-12

Applicant: CLARIPI INC.

Inventor： Si Hwan KIM ,  Jong Hyo KIM

IPC: G06V10/776 ,  G06V10/26 ,  G06V10/774 ,  G06V20/70

Abstract: Disclosed is a method of auditing of artificial intelligence-based medical image segmentation, including: performing preprocessing to generate a preprocessed segmentation image by receiving an input medical image and an output segmentation image provided from a medical image segmentation device and preprocessing the output segmentation image based on the input medical image; generating a heatmap image to generate a segmentation error heatmap image, which includes a segmentation error region in the preprocessed segmentation image, by inputting the preprocessed segmentation image to a deep learning model trained in advance; calculating an error risk to calculate a segmentation error risk for the segmentation error region based on pixel values of the segmentation error heatmap image; and providing auditing information to provide the auditing information for auditing accuracy of the output segmentation image based on the calculated segmentation error risk to a user.

公开(公告)号：US20250022134A1

公开(公告)日：2025-01-16

申请号：US18771496

申请日：2024-07-12

Applicant: CLARIPI INC.

Inventor： Chul Kyun AHN ,  Jong Hyo KIM

IPC: G06T7/00

Abstract: Disclosed is a method of auditing of artificial intelligence-based medical image enhancement, including: performing preprocessing to generate a comparison image based on an input image and an output image received from a medical image enhancement device; generating a heatmap image to generate a hallucination heatmap image including a hallucination region in the comparison image by inputting the comparison image to a deep learning model trained in advance; calculating an error risk to calculate a hallucination risk for the hallucination region based on pixel values of the hallucination heatmap image; and providing auditing information to provide the auditing information for auditing accuracy of the output image based on the calculated hallucination risk to a user.

公开(公告)号：US20250022582A1

公开(公告)日：2025-01-16

申请号：US18771538

申请日：2024-07-12

Applicant: CLARIPI INC.

Inventor： Ah Yeong LEE ,  Jong Hyo KIM

IPC: G16H30/40 ,  G06T11/00 ,  G16H30/20

Abstract: Disclosed is a method of deep learning-based medical image style neutralization for generating a neutralized image to be input to an artificial intelligence-based diagnosis support program includes: obtaining a medical image for processing from an outside; and generating a neutralized image by inputting the medical image for the processing to a style neutralization deep learning model trained in advance to neutralize imaging characteristics of the medical image for the processing, wherein the style neutralization deep learning model includes a plurality of style neutralization deep learning models, and the style neutralization deep learning model corresponding to the imaging characteristics of the medical image for the processing performs the neutralization of the medical image for the processing.

公开(公告)号：US20240169745A1

公开(公告)日：2024-05-23

申请号：US18514637

申请日：2023-11-20

Applicant: CLARIPI INC.

Inventor： Jong Hyo KIM ,  Chang Won KIM ,  Je Myoung LEE ,  Tae Jin KIM

IPC: G06V20/60 ,  G06T5/00 ,  G06T7/11 ,  G06T15/10 ,  G06V10/77 ,  G06V20/50 ,  G16H30/40

CPC classification number: G06V20/60 ,  G06T5/002 ,  G06T7/11 ,  G06T15/10 ,  G06V10/77 ,  G06V20/50 ,  G16H30/40 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/30012 ,  G06T2210/12 ,  G06T2210/41 ,  G06V2201/033

Abstract: The present disclosure provides an apparatus of identifying a vertebral body from a medical image, and the apparatus includes a vertebral bone identification module configured to identify the vertebral body based on a multi-slice medical image provided from an outside, in which the vertebral identification module reconstructs the multi-slice medical image to create a three-dimensional medical image, obtains a coronal projection image for the three-dimensional medical image by projecting the three-dimensional medical image in a coronal plane direction, divides the coronal projection image into a selection area including at least one of a lumbar and a thoracic, obtains area information corresponding to the selection area in the three-dimensional medical image based on the divided selection area, and performs numbering on the vertebral body based on the area information and the three-dimensional medical image.

公开(公告)号：US20220036512A1

公开(公告)日：2022-02-03

申请号：US17505159

申请日：2021-10-19

Applicant: ClariPI Inc. ,  Seoul National University R&DB Foundation

Inventor： Jong Hyo KIM ,  Hyun Sook PARK ,  Tai Chul PARK ,  Chul Kyun AHN

IPC: G06T5/00 ,  G01R33/56 ,  G01R33/565 ,  G01R33/48

Abstract: Provided is a deep learning based accelerated MRI image quality restoring method. The deep learning based accelerated MRI image quality restoring method includes extracting test information from an input accelerated MRI image, selecting at least one deep learning model corresponding to the test information, among a plurality of previously trained deep learning models, and outputting an MRI image with a restored image quality for the input accelerated MRI image with the input accelerated MRI image as an input of at least one selected deep learning model.