The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 28, 2023

Filed:

Apr. 12, 2021
Applicant:

Nantong University, Jiangsu, CN;

Inventors:

Weiping Ding, Jiangsu, CN;

Zhihao Feng, Jiangsu, CN;

Ming Li, Jiangsu, CN;

Ying Sun, Jiangsu, CN;

Yi Zhang, Jiangsu, CN;

Hengrong Ju, Jiangsu, CN;

Jinxin Cao, Jiangsu, CN;

Assignee:

NANTONG UNIVERSITY, Jiangsu, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/10 (2017.01); G16H 30/40 (2018.01); G06V 10/82 (2022.01); G06V 10/776 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); G06T 7/10 (2017.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06V 2201/031 (2022.01);
Abstract

Disclosed is a fully convolutional genetic neural network method for segmentation of infant brain record images. First, infant brain record image data is input and preprocessed, and genetic coding initialization is performed for parameters according to the length of a DMPGA-FCN network weight. Then, m individuals are randomly grouped into genetic native subpopulations and corresponding twin subpopulations are derived, where respective crossover probability and mutation probability pm of all the subpopulations are determined from disjoint intervals; and an optimal initialization value fa is searched for by using a genetic operator. Afterwards, fa is used as a forward propagation calculation parameter and a weighting operation is performed on the feature address featuremap. Finally, a pixel-by-pixel cross-entropy loss is calculated between predicted infant brain record images and standard segmented images to reversely update the weights, thus finally obtaining optimal weights of a network model for segmentation of the infant brain record images.


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