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Weighted bone age evaluation method and system based on deep learning

A technology of deep learning and bone age, applied in the weighted bone age assessment method and system based on deep learning, in the field of bone age assessment, can solve problems such as complexity, impact of assessment results, different assessment levels, etc., and achieve high accuracy

Active Publication Date: 2021-10-29
INNER MONGOLIA UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The above-mentioned evaluation process is not only complicated, but also the subjective factors brought by the evaluators (for example, different evaluators have different degrees of cognition on the grade evaluation of a certain part of the skeleton, resulting in different evaluation grades) and random errors (when manual reading There may be misreading, missing reading, wrong grade, etc.) It will also affect the evaluation results to varying degrees

Method used

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  • Weighted bone age evaluation method and system based on deep learning
  • Weighted bone age evaluation method and system based on deep learning
  • Weighted bone age evaluation method and system based on deep learning

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Experimental program
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Embodiment 1

[0058] As shown in the figure, Embodiment 1 of the present invention provides a weighted bone age assessment method based on deep learning.

[0059] The method includes:

[0060] Preprocessing the X-ray images of the tester's hand bones;

[0061] Roughly segment the preprocessed image to obtain the ROI sets corresponding to different metacarpal and phalanx bones and the ROI sets of the carpal and radius and ulnar bones;

[0062] Input multiple metacarpal and phalanx ROI sets and carpal, radius and ulna ROI sets into the pre-established hand bone fine segmentation model after rough segmentation, and obtain multiple hand bone ROIs after fine segmentation;

[0063] Input the regions of interest of multiple hand bones obtained after subdivision into the pre-established and trained hand bone classification and rating model to obtain the corresponding classification and development level of each hand bone;

[0064] According to the evaluation chart of the developmental maturity of...

Embodiment 2

[0121] Embodiment 2 of the present invention proposes a weighted bone age assessment system based on deep learning. The system includes: hand bone fine segmentation model, hand bone classification and rating model, preprocessing module, rough segmentation module, fine segmentation module, classification and rating Module, RUS-CHN evaluation module, TW3-C Carpal evaluation module and weighted output module; Concrete processing method is the same as embodiment 1, wherein,

[0122] The preprocessing module is used to preprocess the X-ray images of the tester's hand bones;

[0123] The rough segmentation module is used to roughly segment the preprocessed image, and respectively obtain the ROI sets corresponding to different metacarpal and phalanx and the carpal and radius and ulna ROI sets;

[0124] The fine-segmentation module is used to input a plurality of metacarpophalangeal ROI sets and carpal and radius-ulna ROI sets into the pre-established hand bone fine-segmentation model...

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Abstract

The invention discloses a weighted bone age evaluation method and system based on deep learning. The method comprises the following steps: preprocessing an X-ray image of a hand bone of a tester; then carrying out coarse segmentation to respectively obtain a carpal bone and radioulnar bone region-of-interest set and a region-of-interest set corresponding to different metacarpal and phalanx; inputting the roughly segmented region-of-interest set into a hand bone fine segmentation model to obtain a plurality of finely segmented hand bone regions-of-interest; inputting a hand bone classification rating model to obtain a classification and development grade corresponding to each hand bone; according to the metacarpal and phalanx and ulna development maturity evaluation chart, obtaining the bone age evaluated by the RUS-CHN method according to the classification and development grade corresponding to each hand bone; according to the wrist bone development maturity evaluation chart, obtaining the bone age evaluated by the TW3-C Carpal method according to the classification and development grade corresponding to each wrist bone; and carrying out weighted summation on the bone ages evaluated by the two methods to obtain a final bone age evaluation result of the testee.

Description

technical field [0001] The present invention relates to the technical field of intelligent medical image diagnosis, in particular to the field of bone age assessment methods, and in particular to a weighted bone age assessment method and system based on deep learning. Background technique [0002] Bone age refers to the age of physical development determined according to the changes in bone development, and is an important data indicator to measure the bone development degree of children and adolescents. [0003] Generally speaking, pediatric endocrinologists regularly observe X-ray images of children's left hands to estimate the maturity of bones, and then evaluate the growth and development of patients or give treatment measures. Among them, the most commonly used methods include the G&P atlas method, TW2 scoring method, TW3 scoring method derived from foreign countries, and the Zhonghua 05 scoring method derived from China, but reading such X-ray images requires a lot of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11A61B6/00G06K9/32G06K9/62G06N3/08
CPCG06T7/11G06N3/08A61B6/505G06T2207/20081G06T2207/20084G06T2207/30008G06T2207/10116G06F18/241Y02P90/30
Inventor 乔嘉昕孙锴黄威石映崇宋健赵明信
Owner INNER MONGOLIA UNIVERSITY
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