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A bone age evaluation method based on two-stage neural network

A neural network and evaluation method technology, applied in the fields of computer vision and digital image processing, can solve the problems of poor model generalization ability and low evaluation accuracy

Active Publication Date: 2019-02-15
BEIHANG UNIV
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Problems solved by technology

[0005] Previous bone age evaluation methods usually have the disadvantages of poor model generalization ability and low evaluation accuracy

Method used

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  • A bone age evaluation method based on two-stage neural network
  • A bone age evaluation method based on two-stage neural network
  • A bone age evaluation method based on two-stage neural network

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Embodiment Construction

[0047] In order to better understand the technical solutions of the present invention, the implementation manners of the present invention will be further described below in conjunction with the accompanying drawings.

[0048] Flow chart of the present invention is as figure 1 As shown, the present invention is based on a two-stage deep neural network bone age evaluation method, and its specific implementation steps are as follows:

[0049] Step 1: Scale the original input image proportionally, so that the long side is 512, and the short side is filled with 0 to be 512;

[0050] Step 2: Image mask extraction;

[0051] First, build a fully convolutional segmentation network, the network structure is:

[0052] Each downsampling layer uses a 3x3 convolution kernel and uses RELU as the activation function. The number of channels in the first two convolutional layers is 64, the number of channels in the third and fourth layers is 128, the number of channels in the fifth to sevent...

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Abstract

The invention provides a bone age evaluation method based on a two-stage neural network, which comprises the following steps: 1. Adjusting the original input hand bone image size to 512x512, namely scaling according to scale, and adding edge information; 2, extract an image mask; 3, generate a training image; 4, respectively calculating two-dimensional single-scale wavelet transform and Canny operator edge extraction for the image generated in the step 3, and composing three channels of input samples together with the image equalized by the histogram; 5, carrying out data enhancement on the three-channel image generated in the step 4 to expand the data set size; 6. Prediction of bone age; By combining the image segmentation and bone age prediction, the detection method can suppress the noise and segment the hand bone area accurately, so as to evaluate the bone age. For other image processing fields such as target segmentation, target retrieval, target regression prediction and so on, it has practical application value.

Description

(1) Technical field [0001] The invention relates to a bone age evaluation method based on a two-stage neural network, belonging to the fields of computer vision and digital image processing. It has broad application prospects in the fields of target segmentation and target recognition. (2) Background technology [0002] Bone age assessment in children and adolescents is widely used in the clinical, legal and sports medicine fields. Clinically, x-ray images of the hand are often used to assess an individual's maturity. At present, bone age assessment is mainly performed manually by trained radiologists, who manually evaluate the development of hand bones in X-ray images according to the atlas method (Greulich-Pyle, GP) or scoring method (Tanner-Whitehouse, TW). GPs assess bone age by comparing hand x-ray images with an atlas composed of reference images from subjects of different ages. The TW method considers a set of specific regions of interest (Regions of Interest, ROIs...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/04G06F17/50
CPCG06T7/0012G06T2207/20081G06T2207/10116G06T2207/30008G06F30/20G06N3/045
Inventor 刘博周付根初美呈白相志
Owner BEIHANG UNIV
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