Correcting segmentation of medical images using a statistical analysis of historic corrections

US11861846B2 · kind B2 · utility

Assignee

• Brainlab AG · DE

Inventors

• Stefan Vilsmeier · Munich, DE
• Andreas Blumhofer · Neubiberg, DE
• Jens Schmaler · Mering, DE

Key dates

• Filing date: Dec 20, 2019
• Grant date: Jan 2, 2024
• Expiry date: Dec 4, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2207/30004
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

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 l…