公开(公告)号：US12223652B2

公开(公告)日：2025-02-11

申请号：US17749353

申请日：2022-05-20

Applicant: Siemens Healthineers AG

Inventor： Jonathan Sperl ,  Michael Zenge ,  Jens Kaftan

IPC: G06T7/00 ,  G06V40/50 ,  G16H30/40

Abstract: At least one example embodiment relates to a computer-implemented method comprising receiving a medical image dataset; receiving at least one medical comparison image dataset; extracting biometric data based on the medical image dataset; extracting biometric comparison data based on the medical comparison image dataset; determining a measure of difference between the biometric comparison data and the biometric data; and assigning the medical image dataset to the medical comparison image dataset when the measure of difference does not exceed a threshold value.

公开(公告)号：US11935230B2

公开(公告)日：2024-03-19

申请号：US17237160

申请日：2021-04-22

Applicant: Siemens Healthineers AG

Inventor： Philipp Hölzer ,  Richard Frank ,  Sebastian Schmidt ,  Jonathan Sperl

IPC: G06K9/62 ,  G06F18/21 ,  G06F18/2433 ,  G06N5/04 ,  G06N20/00 ,  G06T7/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20

CPC classification number: G06T7/0012 ,  G06F18/2163 ,  G06F18/2433 ,  G06N5/04 ,  G06N20/00 ,  G16H30/20 ,  G16H30/40 ,  G16H40/20 ,  G06V2201/03

Abstract: A system and method for identifying abnormal medical images. The system can be configured to receive a medical image, segment an anatomical structure from the medical image to define a segmented dataset, register the segmented dataset to a baseline dataset defining a normal anatomical structure, classify, by an abnormality classifier, whether the anatomical structure within the medical image as either abnormal or normal, wherein the abnormality classifier comprises a machine learning algorithm trained to distinguish between normal and abnormal versions of the anatomical structure in medical images, and based on whether the anatomical structure can be segmented from the medical image, whether the segmented dataset can be registered to the baseline dataset, or a classification associated with the medical image output by the abnormality classifier, flagging the medical image as either normal or abnormal.