Bone age evaluation method and device based on deep convolutional neural network, and computer readable storage medium

Abstract

The invention provides a bone age evaluation method and device based on a deep convolutional neural network and a computer readable storage medium, and the method comprises the steps: collecting a wrist bone X-ray film calibrated with bone age information, constructing a first deep convolutional neural network based on a UNet algorithm to carry out the image segmentation of the wrist bone X-ray film, so as to obtain a preprocessed image set, partial images are selected as training set images, and the rest images are selected as test set images; constructing a second deep convolutional neural network based on a DenseNet algorithm, and inputting the training set image to train parameters of the second deep convolutional neural network so as to obtain a trained second deep convolutional neural network, the trained second deep convolutional neural network having learned network weight parameters; and inputting the test set image into a trained second deep convolutional neural network, and outputting a bone age evaluation result after iteration of the trained second deep convolutional neural network. After the technical scheme is adopted, the bone age evaluation result can meet the requirements of clinical application.

Description

technical field [0001] The present invention relates to the field of medical image processing, in particular to a bone age assessment method, device and computer-readable storage medium based on a deep convolutional neural network. Background technique [0002] Skeletal age is referred to as bone age, which is an objective index to evaluate the bone development degree of adolescents and children and to measure biological age. Bone age assessment plays an important role in the growth and development of adolescents and children, disease diagnosis, judicial identification, and sports competitions. In terms of growth and development, bone age can give the biological age of children, estimate height, and evaluate whether the growth and development are normal. In terms of disease diagnosis, if the bone age is higher than the chronological age, it belongs to the state of early development, and the doctor will consider whether the individual suffers from diseases such as hyperthyro...

AI Technical Summary

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

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