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Full-automatic bone age evaluation method based on convolutional neural network

A convolutional neural network, fully automatic technology, applied in the field of automatic bone age assessment based on convolutional neural network

Pending Publication Date: 2019-12-10
ZHEJIANG DE IMAGE SOLUTIONS CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The accuracy rate of the currently public fully automatic bone age assessment error that can be found within one year is less than 95%, and clinically it is necessary to control the assessment error within 6 months

Method used

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  • Full-automatic bone age evaluation method based on convolutional neural network
  • Full-automatic bone age evaluation method based on convolutional neural network
  • Full-automatic bone age evaluation method based on convolutional neural network

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Embodiment Construction

[0039] First of all, it should be explained that the present invention relates to database technology, which is an application of computer technology in the field of medical image processing. During the implementation of the present invention, the application of multiple software function modules will be involved. The applicant believes that, after carefully reading the application documents and accurately understanding the realization principle and purpose of the present invention, combined with existing known technologies, those skilled in the art can fully implement the present invention by using their software programming skills. The aforementioned software functional modules include but are not limited to: standardized preprocessing module, convolutional neural network training module, bone age assessment module, etc. All mentioned in the application documents of the present invention belong to this category, and the applicant will not list them one by one.

[0040] The m...

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Abstract

The invention relates to the field of medical image processing, and aims to provide a full-automatic bone age evaluation method based on a convolutional neural network. The method comprises the stepsof performing standardized preprocessing by utilizing a wrist bone X-ray film in a database, and preparing a training set; constructing and training a convolutional neural network; and performing dataprocessing on the new bone age slice by using the trained convolutional neural network to obtain a bone age evaluation result. According to the invention, the bone age is evaluated by means of the convolutional neural network, and the export of the evaluation result can be automatically realized. According to the method, the defect of key feature loss caused by over-segmentation of the wrist boneimage in a full-automatic segmentation method in the prior art can be overcome, the accuracy rate of an automatic evaluation result within 6 months reaches 80%, the accuracy rate of the error within1 year reaches 95%, and the method meets the acceptable requirements of clinical application.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a fully automatic bone age assessment method based on a convolutional neural network. Background technique [0002] Bone age is an objective index to evaluate the degree of skeletal development of adolescents and children and to measure biological age. At present, bone age is widely used in the treatment and detection of diseases affecting the growth and development of adolescents and children. Bone age can be used to evaluate and analyze whether the physical growth is consistent with the calendar age, so as to detect growth deviation early; the bone age can be used to predict the pubertal development, and the difference between the bone age and the calendar age can be used to indirectly understand the growth potential of adolescents and children; It helps to diagnose some pediatric endocrine diseases; in addition, bone age provides scientific and objective legal basis fo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06N3/08G06T2207/10116G06T2207/20084G06T2207/20081G06T2207/30008G06N3/045G06F18/24G06F18/214
Inventor 王守超蔡祁文
Owner ZHEJIANG DE IMAGE SOLUTIONS CO LTD
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