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An X-ray film hand bone maturity interpretation method based on a deep neural network

A deep neural network and maturity technology, applied in the field of intelligent bone maturity interpretation, can solve problems such as low precision and poor result stability, and achieve the effects of improving efficiency, eliminating differences, and improving interpretation accuracy

Active Publication Date: 2019-06-28
浙江飞图影像科技有限公司
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Problems solved by technology

[0004] In order to solve the shortcomings of low precision and poor result stability in the existing technology, the present invention proposes a high-precision, high-stability bone age classification and evaluation method based on deep learning

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  • An X-ray film hand bone maturity interpretation method based on a deep neural network
  • An X-ray film hand bone maturity interpretation method based on a deep neural network
  • An X-ray film hand bone maturity interpretation method based on a deep neural network

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Embodiment Construction

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] refer to Figure 1 to Figure 5 , a method for interpreting the maturity of X-ray hand bones based on a deep neural network, which includes six steps in the operation process. The details of each module are given in the figure below, including the following steps:

[0038] Step 1: Perform unified preprocessing on the original hand bone X-ray image, unify the gray value distribution of the X-ray film, and brighten the darker hand bone X-ray image to obtain Output1;

[0039] Step 2: Take Output1 as the input object, train the model M1, extract the key whole hand bone from the X-ray image of the hand bone, remove the noise, and unify the size to obtain the key hand bone image Output2;

[0040] Step 3: Using the Faster-RCNN model, train the sampling data of 13 key bone blocks to obtain model M2, so that it can be accurately segmented to obtain 13 key bone blocks, an...

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Abstract

The invention discloses an X-ray film hand bone maturity interpretation method based on a deep neural network, and the method comprises the steps: carrying out the unified preprocessing of an originalhand bone image, and improving an X-ray film with a poor color to obtain Output1; sampling and training the model M1 to obtain an Output2; training Faster-RCNN model, and segmenting 13 key bone blocks to obtain Output 3 in accordance with RUS- CHN standard; taking Output3 as an input data format standard, sampling and training a model M3, and providing a local feature map F1; taking Output2 as aninput data format standard, sampling and training a model M4, and providing a global feature map F2; combining the M3 model and the M4 model in a manner of connecting F1 and F2 in parallel; and training the model M5 to enable the model M5 to output an optimal bone maturity interpretation result by learning the two characteristic patterns F1 and F2. The maturity score of each key bone block of thehand bone X-ray film can be automatically obtained.

Description

technical field [0001] The present invention relates to the field of medical image analysis and machine learning, in particular to an intelligent bone maturity interpretation method applied to X-ray images of human hand bones, belonging to the field of medical image analysis based on deep learning. Background technique [0002] Skeletal age, referred to as bone age, is determined by the degree of bone calcification in children. Bone age is one of the important indicators to measure children's growth and development, and has a wide range of application values. As an important indicator and parameter of human growth and development, bone age has been widely used in the evaluation of growth and development of children and adolescents, the diagnosis and treatment of endocrine diseases in children, and the selection of athletes. [0003] Traditionally, radiologists have measured bone age in children by comparing x-rays of the child's hands to a standard for their age. Bone age ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/62G06N3/04G06N3/08
Inventor 郝鹏翼谢旭杭徐震宇高翔李芝禾吴福理吴健
Owner 浙江飞图影像科技有限公司
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