Adaptive classification for whole slide tissue segmentation

US10102418B2 · kind B2 · utility

Assignee

• Ventana Medical Systems, Inc. · US

Inventors

• Joerg Bredno · San Francisco, US
• Christophe Chefd'hotel · Jersey City, US
• Ting Chen · Sunnyvale, US
• Srinivas Chukka · San Jose, US
• Kien Nguyen · Ngọc Lặc, VN

Key dates

• Filing date: Jul 28, 2016
• Grant date: Oct 16, 2018
• Expiry date: Nov 12, 2036

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2207/30024
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the whole-slide (WS) image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training database, and classification confidence scores for the GPs are generated. The classified GPs with high confidence scores are utilized to generate an adaptive training database, which is then used to re-classify the low confidence GPs. The motivation of the method is that the strong variation of tissue appearance makes the classification problem more challenging, while good classification results are obtained when the training and test data origin from the same slide.