Bone age evaluation method for realizing image segmentation and classification based on deep learning

A technology of image segmentation and deep learning, which is applied in the field of bone age assessment, automatic segmentation and recognition of X-ray image targets, and automatic bone age assessment based on deep learning to realize image segmentation and classification. The effect of error, accelerated recognition rate, and strong generalization ability

Active Publication Date: 2020-04-28
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, most studies do not take into account the fact that other objects besides hand bones (e.g., X-ray labels and annotation markers) also exist in X-ray images

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  • Bone age evaluation method for realizing image segmentation and classification based on deep learning
  • Bone age evaluation method for realizing image segmentation and classification based on deep learning
  • Bone age evaluation method for realizing image segmentation and classification based on deep learning

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Embodiment Construction

[0033] The embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and provides detailed implementation methods and specific operation processes.

[0034] Such as figure 1 As shown, this embodiment includes the following steps:

[0035] Step 1, use digital image processing method to preprocess the sample data. The dataset comes from the RSNA competition and contains 12611 image samples, including 5778 female and 6833 male images, with a bone age distribution of 1-228 months. The images in the data set have certain noise and background interference, and the distribution of gray levels is uneven. Image preprocessing is required. There are four main steps: 1) Use Gaussian filtering to remove the noise existing in the image; 2) Enter the histogram of the image Equalization, improve the contrast of the image, and make the ...

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Abstract

The invention discloses a bone age evaluation method for realizing image segmentation and classification based on deep learning. The method comprises the following steps of: 1, processing a data set by using a digital image processing method to obtain sample data with higher quality; 2, manually marking a part of hand bone images, training an image segmentation network U-Net by using the part of images, then segmenting the data set by using the trained U-Net to obtain a background-removed data set, and making a training set, a verification set and a test set according to a certain proportion;3, training an improved image classification network VGG16 by using the processed data set; and 4, testing the trained model by using the test set and evaluating a result. Compared with an original bone age evaluation method, the improvement method provided by the invention effectively improves the accuracy of evaluating the hand bone image by the model, and has higher efficiency at the same time.

Description

technical field [0001] The present invention relates to the technical field of medical imaging intelligent diagnosis, in particular to the field of automatic segmentation and recognition of X-ray image targets and bone age assessment methods, and in particular to a fully automatic bone age assessment method based on deep learning to realize image segmentation and classification. Background technique [0002] Bone age represents the level of growth and development of a child characterized by a certain age, and it shows a completely consistent relationship with the maturity of the individual's physical development, and is the most reliable indicator for evaluating the growth and development of an individual. Bone age assessment is a commonly used clinical method in the study of endocrine, genetic and growth disorders in adolescents and children. Skeletal age assessment has a wide range of applications in the development status and prediction of adolescents, family planning, ta...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/06G06N3/08G06T7/155
CPCG06T7/0012G06T7/155G06N3/061G06N3/08G06T2207/10116G06T2207/30008G06T2207/20172G06N3/045G06F18/241
Inventor 高云园朱涛高博王翔坤甘海涛张启忠
Owner HANGZHOU DIANZI UNIV
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