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：US20250166132A1

公开(公告)日：2025-05-22

申请号：US18811465

申请日：2024-08-21

Applicant: CLARIPI INC.

Inventor： Jong Hyo KIM ,  Tae Jin KIM ,  Chul Kyun AHN

IPC: G06T5/50 ,  G06T5/70 ,  G16H30/40

Abstract: Disclosed is an apparatus for denoising a medical image, including: an image processing module configured to extract a noise component from a medical image for processing by inputting the medical image for the processing to a noise extraction deep learning model trained in advance, and generate a noise-removed image by subtracting the noise component from the medical image for the processing, wherein the noise extraction deep learning model is trained using a simulation noise component image generated by a noise simulator and a simulation low-quality image generated based on the simulation noise component image as a pair.

公开(公告)号：US11328394B1

公开(公告)日：2022-05-10

申请号：US17380429

申请日：2021-07-20

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 ,  G06T5/50 ,  G06N20/00

Abstract: Provided is a deep learning based contrast-enhanced (CE) CT image contrast amplifying method and the deep learning based CE CT image contrast amplifying method includes extracting at least one component CT image between a CE component and a non-CE component for an input CE CT image with the input CE CT image as an input to a previously trained deep learning model; and outputting a contrast-amplified CT image with respect to the CE CT image based on the input CE CT image and the at least one extracted component CT image.

公开(公告)号：US10891762B2

公开(公告)日：2021-01-12

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：US20230029394A1

公开(公告)日：2023-01-26

申请号：US17868314

申请日：2022-07-19

Applicant: CLARIPI INC.

Inventor： Hyun Sook PARK ,  Chang Yong HEO ,  Tae Jin KIM ,  Tae Yoon LIM ,  Je Myoung LEE

IPC: G06T7/00 ,  G06T3/40 ,  G16H30/40

Abstract: Disclosed are an apparatus and method for medical image processing according to pathologic lesion properties, the method including: recognizing a readout area different from an original readout area in a medical image by applying a previously trained deep learning model to the medical image, extracting properties, which include at least one of a location and a size of the readout area, from the medical image, and generating a readout image for the readout area, which is different from the original readout area corresponding to a purpose of taking the medical image, by reconstructing the medical image, thereby having an effect on generating a readout image for a different kind of pathologic lesion from a previously acquired medical image.

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：US20250022130A1

公开(公告)日：2025-01-16

申请号：US18771565

申请日：2024-07-12

Applicant: Claripi Inc.

Inventor： Dong Ok KIM ,  Jong Hyo Kim

IPC: G06T7/00

Abstract: According to the disclosure, the image generating module generates a second multi-slice medical image set having a low resolution in a preset direction by reconstructing a first multi-slice medical image set received from an outside to have a cutting plane perpendicular to a slice plane in the preset direction, generates a third multi-slice medical image set, of which a resolution is improved as much as an integer multiple in the preset direction in slice images included in the second multi-slice medical image set through a deep learning model trained in advance by inputting the second multi-slice medical image set to the deep learning model, and generates and outputs a 3D high-resolution image improved in resolution based on the third multi-slice medical image set.

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.