Patent · US Active

Deep reinforcement learning for recursive segmentation

US10733788B2 · kind B2 · utility

4Cited by

1References

19Claims

0Family size

Assignee

Siemens Healthcare Limited · DE

Inventors

Pascal Ceccaldi · New York, US
Xiao Chen · Somerville, US
Boris Mailhe · Plainsboro, US
Benjamin L. Odry · West New York, US
Mariappan S. Nadar · Plainsboro, US

Key dates

Filing date	Jan 18, 2019
Grant date	Aug 4, 2020
Priority date	—
Expiry date	Jan 18, 2039

Classification

Technology area (CPC G)Physics
CPC primaryG06T2207/30016
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Systems and methods are provided for generating segmented output from input regardless of the resolution of the input. A single trained network is used to provide segmentation for an input regardless of a resolution of the input. The network is recursively trained to learn over large variations in the input data including variations in resolution. During training, the network refines its prediction iteratively in order to produce a fast and accurate segmentation that is robust across resolution differences that are produced by MR protocol variations.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.