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X-ray film bone age prediction method and system based on deep learning

A prediction method and deep learning technology, applied in image data processing, instruments, calculations, etc., can solve the problems of complex application of bone development grade standard text description, large random error, strong subjectivity, etc., to solve the cumbersome evaluation process, random The effect of large error and strong subjectivity

Active Publication Date: 2018-03-06
XIAN UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0008] The purpose of the present invention is to solve the problems of excessive subjectivity, large random error, complex application of text description of bone development grade standard and cumbersome process in traditional bone age evaluation using "scoring method" and "atlas method", and proposes a A fully automatic bone age assessment method based on X-ray film training model of adolescent hand bones

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  • X-ray film bone age prediction method and system based on deep learning
  • X-ray film bone age prediction method and system based on deep learning

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[0028] Combine below figure 1 , figure 2 As well as specific embodiments to further describe the embodiments of the present invention.

[0029] figure 1Shown is an embodiment of the flow chart of the X-ray film bone age prediction method based on deep learning of the present invention, including the following steps: (1) The step of sample data preprocessing, including cleaning the X-ray film data of hand bones of teenagers, and labeling samples . The training sample data comes from the public data set of the National Institutes of Health (http: / / www.ipilab.org / BAAweb / ), the RSNA competition (http: / / rsnachallenges.cloudapp.net / competitions / 4#learn_the_details-overview ) and hospitals. There are more than 11,400 left-handed X-ray films of different races with normal development from 1 to 18 years old in the data set. It also includes relevant demographic data and reading information of each X-ray film by pediatricians and radiologists. The images are classified by age . T...

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Abstract

The invention provides an X-ray film bone age prediction method and system based on deep learning. According to the invention, a bone age prediction result is finally formed through performing preprocessing on an X-ray image of a teenager, automatic segmentation of a hand bone area and bone age prediction. The method comprises a hand bone X-ray film preprocessing and sample data enhancement method, a hand bone X-ray film image block sampling method, a hand bone automatic segmentation algorithm and a transfer learning based hand bone X-ray film bone age assessment algorithm, and finally designsa hand bone X-ray film bone age prediction system taking Dicom data as input. Users only need to select a hand bone X-ray film with the bone age to be predicted, the segmentation and prediction process is completely automatic, and doctors are not required to perform region marking or selecting, and a powerful tool is provided for bone age assessment in scientific research and clinical practice.

Description

technical field [0001] The present invention relates to the technical field of intelligent medical image diagnosis, in particular to the field of X-ray film automatic target analysis and recognition, bone age testing method, and in particular to a fully automatic X-ray film bone age prediction method based on deep learning technology. Background technique [0002] Bone age is the abbreviation of skeletal maturity age. Bone age evaluation is a method and technology for evaluating the maturity of individual skeletal development based on the common characteristics of skeletal development in a specific population. The maturity of the bones will be manifested in different parts of the human body, especially in the wrist. These features are universal and irreversible. During the evaluation, compare the bone X-ray images with the standard bone development atlas. If there are differences, you need to Further diagnosis and evaluation, bone age evaluation is currently the most accurat...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/10116G06T2207/20081G06T2207/20084G06T2207/30008
Inventor 贾阳杨斌王萌路玉峰韩俊刚张帅苟凡张倩妮
Owner XIAN UNIV OF POSTS & TELECOMM
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