Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Double-bone-age evaluation method based on joint global and local convolutional neural network features

A convolutional neural network and local feature technology, applied in the field of image processing, can solve the problem of low accuracy of bone age assessment, achieve high accuracy, concise and easy-to-understand purpose and advantages, and improve performance

Active Publication Date: 2022-06-24
杭州健培科技有限公司
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, it is urgent to invent a more effective bone age assessment method to solve the problem of low accuracy of bone age assessment based on image analysis

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Double-bone-age evaluation method based on joint global and local convolutional neural network features
  • Double-bone-age evaluation method based on joint global and local convolutional neural network features
  • Double-bone-age evaluation method based on joint global and local convolutional neural network features

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0033] The embodiment of the present application provides a double bone age assessment method based on joint global and local convolutional neural network features, refer to figure 1 , the method includes the following steps S101 to S106:

[0034] S101. Obtain a full picture of the hand bone corresponding to the tester, gender information, and an epiphyseal region image corresponding to each epiphyseal region to be rated extracted from the full hand bone picture.

[0035] S102. Construct a double-bone age prediction model combining global and local convolutional neural network features. The double-bone age prediction model includes a first convolutional neural network, a second convolutional neural network, and a GCN network, and the first convolutional neural network is used for the whole image Feature extraction, the second convolutional neural network and GCN network are used for local feature extraction, and the double-bone age prediction model is gradually and alternately...

Embodiment 2

[0091] Based on the same idea, refer to Figure 4 , this application also proposes a bone age assessment device based on a multi-task convolutional neural network, including:

[0092] The acquiring module 401 is used to acquire the full picture of the hand bone corresponding to the tester, the gender information, and the image of the epiphyseal region corresponding to each epiphyseal region to be rated extracted from the full picture of the hand bone;

[0093] The model building module 402 is used to construct a double-bone age prediction model combining global and local convolutional neural network features. The double-bone age prediction model includes a first convolutional neural network, a second convolutional neural network and a GCN network, and the first convolutional neural network. The network is used for full-image feature extraction, the second convolutional neural network and GCN network are used for local feature extraction, and the double-bone age prediction mode...

Embodiment 3

[0101] This embodiment also provides an electronic device, refer to Figure 5 , comprising a memory 504 and a processor 502, where a computer program is stored in the memory 504, and the processor 502 is configured to run the computer program to perform the steps in any of the above method embodiments.

[0102] Specifically, the above-mentioned processor 502 may include a central processing unit (CPU), or a specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0103] Among others, memory 504 may include mass storage 504 for data or instructions. By way of example and not limitation, the memory 504 may include a hard disk drive (Hard Disk Drive, referred to as HDD), floppy disk drive, solid state drive (Solid State Drive, referred to as SSD), flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (Uni...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a double-bone age evaluation method based on joint global and local convolutional neural network features. The method comprises the following steps: acquiring a hand bone total graph and gender information corresponding to a tester, and an epiphysis area image corresponding to each epiphysis area to be rated extracted from the hand bone total graph; a bone age assessment task and an anatomical local epiphyseal area maturity rating task are combined to utilize global features and local features at the same time, so that the performance of the bone age assessment method is improved.

Description

technical field [0001] The present application relates to the technical field of image processing, and in particular, to a double-bone age assessment method based on joint global and local convolutional neural network features. Background technique [0002] Bone age assessment is a common clinical diagnosis designed to assess the biological maturity of the human skeleton. Accurate bone age assessment is not only an important indicator for estimating individual final height, but also a powerful tool to assist in the diagnosis and treatment of pediatric endocrinology and pediatric orthopedic diseases. [0003] The current mainstream methods for bone age assessment are based on hand X-ray images of the non-dominant hand (usually the left hand). The GP atlas method compares an individual's hand X-rays with reference images of different bone ages to obtain bone age estimates; the TW scoring method and the Zhonghua 05 scoring method are used to grade the maturity of different ana...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/80G06V10/44G06V10/42G06V10/82G06K9/62G06N3/04G06N3/08G06T7/00
CPCG06N3/08G06T7/0012G06T2207/30008G06N3/045G06F18/253
Inventor 何林阳季红丽程国华
Owner 杭州健培科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com