Invention Grant
- Patent Title: Correcting segmentation of medical images using a statistical analysis of historic corrections
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Application No.: US17281493Application Date: 2019-12-20
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Publication No.: US11861846B2Publication Date: 2024-01-02
- Inventor: Stefan Vilsmeier , Andreas Blumhofer , Jens Schmaler
- Applicant: Brainlab AG
- Applicant Address: DE Munich
- Assignee: BRAINLAB AG
- Current Assignee: BRAINLAB AG
- Current Assignee Address: DE Munich
- Agency: Gray Ice Higdon
- International Application: PCT/EP2019/086789 2019.12.20
- International Announcement: WO2021/121631A 2021.06.24
- Date entered country: 2021-03-30
- Main IPC: G06T7/143
- IPC: G06T7/143 ; G06T7/174 ; G06T7/00 ; G06T7/11 ; G06N3/08 ; G06N20/20

Abstract:
Disclosed is a computer-implemented methods of determining distributions of corrections for correcting the segmentation of medical image data, determining corrections for correcting the segmentation of medical image data, training a learning algorithm for determining a segmentation of a digital medical image, and determining a relation between an image representation of the anatomical body part in an individual medical image and a label to be associated with the image representation of the anatomical body part in the individual medical image using the trained machine learning algorithm. The methods encompass reading a plurality of corrections to image segmentations, wherein the corrections themselves may have been manually generated, transforming these corrections into a reference system which is not patient-specific such as an atlas reference system, conducting a statistical analysis of the correction, and applying the re-transformed result of the statistical analysis to patient images. The result of the statistical analysis may also be used to appropriately train a machine learning algorithm for automatic segmentation of patient images. The application of such a trained machine learning algorithm is also part of this disclosure.
Public/Granted literature
- US20220122266A1 CORRECTING SEGMENTATION OF MEDICAL IMAGES USING A STATISTICAL ANALYSIS OF HISTORIC CORRECTIONS Public/Granted day:2022-04-21
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