Patent · US Active

Multi-level convolutional LSTM model for the segmentation of MR images

US11030750B2 · kind B2 · utility

3Cited by

0References

20Claims

0Family size

Assignees

Merck Sharp & Dohme LLC · US
MSD INTERNATIONAL GMBH · CH

Inventors

Antong Chen · Blue Bell, US
Dongqing Zhang · New York, US
Ilknur Icke · Attleboro, US
Belma Dogdas · Ridgewood, US
Sarayu Parimal · Singapore, SG

Key dates

Filing date	May 30, 2019
Grant date	Jun 8, 2021
Priority date	—
Expiry date	Aug 19, 2039

Classification

Technology area (CPC G)Physics
CPC primaryG06V2201/03
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Approaches for the automatic segmentation of magnetic resonance (MR) images. Machine learning models segment images to identify image features in consecutive frames at different levels of resolution. A neural network block is applied to groups of MR images to produce primary feature maps at two or more levels of resolution. The images in a given group of MR images may correspond to a cycle and have a temporal order. A second RNN block is applied to the primary feature maps to produce two or more output tensors at corresponding levels of resolution. A segmentation block is applied to the two or more output tensors to produce a probability map for the MR images. The first neural network block may be a convolutional neural network (CNN) block. The second neural network block may be a convolutional long short-term (LSTM) block.

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