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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
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.
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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
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.
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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
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.
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4.发明申请公开(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.
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5.发明申请公开(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.
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Patent Agency Ranking
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
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.
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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
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.
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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.
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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
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.
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公开(公告)号：US20230215538A1
公开(公告)日：2023-07-06
申请号：US18148286
申请日：2022-12-29
Applicant: CLARIPI INC.
Inventor： Hyun Sook PARK , Chul Kyun AHN , Tae Jin KIM
CPC classification number: G16H20/17 , G06T7/0012 , G06T2207/10081
Abstract: Disclosed are an optimization method and system for a personalized contrast test based on deep learning, in which a contrast medium optimized for each individual patient is injected to implement optimum pharmacokinetic characteristics in a process of acquiring a medical image, the method including: obtaining drug information of a contrast medium and body information of a patient, in a contrast enhanced computed tomography (CT) scan; generating injection information of the drug to be injected into the patient by a predefined algorithm based on the drug information and the body information; injecting the drug into the patient based on the injection information, and acquiring a medical image by scanning the patient; and amplifying a contrast component in the medical image by inputting the medical image to a deep learning model trained in advance.
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