Deep learning-based multi-site, multi-primitive segmentation for nephropathology using renal biopsy whole slide images

US11645753B2 · kind B2 · utility

Assignees

• CASE WESTERN RESERVE UNIVERSITY · US
• The Cleveland Clinic Foundation · US

Inventors

• Anant Madabhushi · Beachwood, US
• Catherine Jayapandian · Strongsville, US
• Yijiang Chen · Chelmsford, US
• Andrew Janowczyk · East Meadow, US
• John Sedor · Shaker Heights, US
• Laura Barisoni · Durham, US

Key dates

• Filing date: Sep 25, 2020
• Grant date: May 9, 2023
• Expiry date: Mar 18, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2207/30101
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Embodiments discussed herein facilitate segmentation of histological primitives from stained histology of renal biopsies via deep learning and/or training deep learning model(s) to perform such segmentation. One example embodiment is configured to access a first histological image of a renal biopsy comprising a first type of histological primitives, wherein the first histological image is stained with a first type of stain; provide the first histological image to a first deep learning model trained based on the first type of histological primitive and the first type of stain; and receive a first output image from the first deep learning model, wherein the first type of histological primitives is segmented in the first output image.