Tissue characterization based on machine learning in medical imaging

US10748277B2 · kind B2 · utility

Assignees

• Siemens Healthcare Limited · DE
• THE JOHNS HOPKINS UNIVERSITY · US

Inventors

• Shaohua Kevin Zhou · Plainsboro, US
• David Liu · San Francisco, US
• Berthold Kiefer · Erlangen, DE
• Atilla Peter Kiraly · Plainsboro, US
• Benjamin L. Odry · West New York, US
• Robert Grimm · Nuremberg, DE
• Li Pan · Perry Hall, US
• Ihab Kamel · Ellicott City, US

Key dates

• Filing date: Sep 9, 2016
• Grant date: Aug 18, 2020
• Expiry date: Jun 15, 2038

Classification

• Technology area (CPC G): Physics
• CPC primary: G16H50/70
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

Tissue is characterized using machine-learnt classification. The prognosis, diagnosis or evidence in the form of a similar case is found by machine-learnt classification from features extracted from frames of medical scan data. The texture features for tissue characterization may be learned using deep learning. Using the features, therapy response is predicted from magnetic resonance functional measures before and after treatment in one example. Using the machine-learnt classification, the number of measures after treatment may be reduced as compared to RECIST for predicting the outcome of the treatment, allowing earlier termination or alteration of the therapy.