A wrist bone interest region cutting method based on machine learning

A region of interest and machine learning technology, which is applied to instruments, computer components, character and pattern recognition, etc., can solve the problem that the size of the cutting frame cannot be uniformly fixed, and achieve the effect of improving the recognition rate

Active Publication Date: 2019-06-28
浙江康体汇科技有限公司
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Problems solved by technology

[0004] The purpose of the present invention is to overcome the problem that the size of the cutting frame required by different heights and ages of the wrist bone medical image cannot be uniformly fixed, and to segment the wrist bone medical image based on machine learning, and to provide an automatic cutting method for bone age image size self-adaptation

Method used

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Examples

Experimental program
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Embodiment

[0015] The present embodiment provides a method for cutting a region of interest of wrist bone based on machine learning, comprising the following steps:

[0016] 1) Select a batch of wrist bone samples of different ages and heights according to the actual situation, and for each sample piece, calibrate the geometric center points of the 14 bones targeted by the CHN bone age assessment method; the 14 bones are respectively For: radius, palm 1, palm 3, palm 5, near 1, near 3, near 5, middle 3, middle 5, far 1, far 3, far 5, capitate, hamate;

[0017] 2) For each sample piece, based on the geometric center point calibrated in the previous step, further calibrate a rectangular cutting area with appropriate length and width for each bone and can completely wrap the image information required for bone age grade identification, and complete the next step. The production of data sets to be used in the machine learning of the steps;

[0018] 3) Take the age and height of each wrist b...

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Abstract

The invention discloses a wrist bone interest region cutting method based on machine learning, which utilizes a machine learning method to segment a medical image, and achieves a better effect throughthe combination of central point cutting and cutting frame size self-adaption. According to the method, on the premise that it is guaranteed that the size of each individual is independent, more accurate cutting and feature extraction are conducted on each wrist bone picture. According to the method, the self-adaptive cutting frame can be generated for any height and age, and data support is provided for more accurate judgment of the wrist bone grade. The method can be applied to feature region cutting in the field of image recognition, and the recognition rate of the deep learning network isgreatly increased.

Description

technical field [0001] The present invention relates to the technical field of self-adaptive cutting of medical pictures, in particular to a method for cutting regions of interest of wrist bones based on machine learning. Background technique [0002] Now deep learning has very broad prospects in image recognition, natural language processing, speech recognition and other fields. With the wide application of deep learning technology in medical images, automatic assessment of bone age has become a hot spot. [0003] Based on variance analysis and automatic weighting, the ensemble average method of maturity, that is, the CHN method, is an evaluation method for Chinese wrist bones. This method makes a corresponding grade evaluation for 14 wrist bones. Combining the deep learning network to learn the data set of 14 wrist bone medical images to achieve the purpose of automatically judging the bone age. Due to the images of wrist bone age films of different heights and ages, the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/32G06K9/34
Inventor 毛科技丁维龙陈立建周贤年丁潇
Owner 浙江康体汇科技有限公司
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