Progressive and multi-path holistically nested networks for segmentation

US11195280B2 · kind B2 · utility

Assignee

• The United States of America, as represented by the Secretary, Department of Health and Human Services · US

Inventors

• Adam P Harrison · Bethesda, US
• Ziyue Xu · Iowa City, US
• Le Lu · Doylestown, US
• Ronald M. Summers · Potomac, US
• Daniel Joseph Mollura · Bethesda, US

Key dates

• Filing date: Jun 8, 2018
• Grant date: Dec 7, 2021
• Expiry date: Jul 4, 2038

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2207/30061
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Methods include processing image data through a plurality of network stages of a progressively holistically nested convolutional neural network, wherein the processing the image data includes producing a side output from a network stage m, of the network stages, where m>1, based on a progressive combination of an activation output from the network stage m and an activation output from a preceding stage m−1. Image segmentations are produced. Systems include a 3D imaging system operable to obtain 3D imaging data for a patient including a target anatomical body, and a computing system comprising a processor, memory, and software, the computing system operable to process the 3D imaging data through a plurality of progressively holistically nested convolutional neural network stages of a convolutional neural network.