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：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：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

IPC: G16H20/17 ,  G06T7/00

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.

公开(公告)号：US20230214972A1

公开(公告)日：2023-07-06

申请号：US18148267

申请日：2022-12-29

Applicant: CLARIPI INC.

Inventor： Hyun Sook PARK ,  Tae Jin KIM ,  Chul Kyun AHN

IPC: G06T5/20 ,  G06T5/00 ,  G06T7/20

CPC classification number: G06T5/20 ,  G06T5/002 ,  G06T7/20 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20221 ,  G06T2207/20224 ,  G06T2207/30004

Abstract: Disclosed are a motion compensation processing apparatus and method of medical images, in which motion of organs is corrected, the method including: acquiring the medical image and combining an organ motion component into the medical image; training at least one deep learning model based on the medical image combined with the organ motion component so that the deep learning model can remove the organ motion component; and acquiring a processing medical image, selecting a deep learning model corresponding to an organ included in the processing medical image, and removing a motion component for the organ.