CN110555846A - full-automatic bone age assessment method based on convolutional neural network - Google Patents
full-automatic bone age assessment method based on convolutional neural network Download PDFInfo
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- 210000000707 wrist Anatomy 0.000 claims abstract description 10
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- 238000000605 extraction Methods 0.000 claims description 6
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- 238000011176 pooling Methods 0.000 claims description 3
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Abstract
the invention relates to the field of medical image processing, and aims to provide a full-automatic bone age assessment method based on a convolutional neural network. The method comprises the following steps: carrying out standardized preprocessing by using the wrist bone X-ray film in a database to prepare a training set; constructing and training a convolutional neural network; and carrying out data processing on the new bone age tablets by using the trained convolutional neural network to obtain a bone age evaluation result. The invention estimates the bone age by means of the convolutional neural network, and can automatically realize the derivation of the estimation result. The method can overcome the defect that the key characteristics are lost due to the fact that the wrist bone image is subjected to over-segmentation by a full-automatic segmentation method in the prior art, and the accuracy of an automatic evaluation result within 6 months of error reaches 80%, and the accuracy within 1 year of error reaches 95%, so that the method meets the acceptable requirements of clinical application.
Description
Technical Field
the invention relates to the field of medical image processing, in particular to a full-automatic bone age assessment method based on a convolutional neural network.
Background
the bone age is an objective index for evaluating the bone development degree of teenagers and children and measuring the biological age. At present, the bone age is widely applied to the treatment and detection of diseases affecting the growth and development of teenagers and children. Whether the body growth is consistent with the calendar age can be evaluated and analyzed through bone age so as to find the growth deviation in an early stage; the growth potential of the teenagers and children can be indirectly known through the difference between the bone age and the calendar age by predicting the development condition of the adolescence through the bone age; some pediatric endocrine disorders can be diagnosed helped by bone age determination; in addition, the bone age provides scientific and objective legal basis for judicial identification, population birth time estimation and the like. At present, the bone age in China is mainly determined by shooting bone age tablets and then manually evaluating the bone age by bone age experts. The manual interpretation has the defects of large workload, long measuring period, strong subjectivity and the like. Therefore, a good automatic bone age assessment method is urgently needed.
the existing full-automatic segmentation method basically considers the evaluation based on G-P or TW standard, hand carpal bones or characteristic bone blocks need to be extracted, over-segmentation usually occurs, and part of important features may be lost. The minimum error of the fully automatic bone age assessment disclosed at present can be inquired that the accuracy rate in one year is less than 95%, and the assessment error needs to be controlled within 6 months clinically.
Therefore, it is necessary to provide a fully automatic bone age assessment method that can meet clinical requirements for clinical medical applications.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects in the prior art and provides a full-automatic bone age assessment method based on a convolutional neural network.
in order to solve the technical problem, the solution of the invention is as follows:
the invention provides a full-automatic bone age assessment method based on a convolutional neural network, which comprises the following steps of:
(1) carrying out standardized preprocessing by using the wrist bone X-ray film in a database to prepare a training set;
(2) Constructing and training a convolutional neural network;
(3) and carrying out data processing on the new bone age tablets by using the trained convolutional neural network to obtain a bone age evaluation result.
in the present invention, the step (1) specifically includes:
(1.1) collecting bone age tablets with calibrated bone age from a radiology examination database of a hospital, selecting left-hand positive tablets with clear images and complete data, and uniformly processing the left-hand positive tablets to show bright foreground and dark background;
(1.2) carrying out image digital sampling on the collected bone age tablets, wherein the size of the image is 512 x 512, and then realizing segmentation and extraction of hand images by a full-automatic segmentation method;
(1.3) according to the labeled bone age corresponding to the bone age tablets, calibrating the category of the image data obtained by sampling according to the calibrated bone age by taking m months as a step length, wherein m is more than or equal to 1 and less than or equal to 6.
in the present invention, the step (2) specifically includes:
(2.1) randomly selecting 90% from the preprocessed wrist bone data as a training set, and performing data augmentation on the training set;
(2.2) constructing a deep convolutional neural network for learning training; the convolutional neural network is alternately realized by a plurality of convolutional layers, a feature extraction block and a pooling layer;
(2.3) inputting the training set prepared in the step (2.1) into the convolutional neural network in the step (2.2), and training parameters in the convolutional neural network; loss function values are reduced and network weight parameters are updated through training, and after a plurality of times of training, the learned network weight parameters are obtained.
in the present invention, in the training in the step (2.3), classification is performed according to the sex of the subject in the bone age patch calibration data.
in the present invention, the step (3) specifically includes:
(3.1) carrying out standardized pretreatment on the bone age slices of the testee to obtain a clear and complete left-hand positive slice image with the size of 512 multiplied by 512, and processing the left-hand positive slice image to show bright foreground and dark background;
(3.2) inputting the image data obtained in the step (3.1) into a trained convolutional neural network, applying a training set corresponding to the sex of the testee to train learned network weight parameters through iteration of the convolutional neural network, and outputting data corresponding to the category;
and (3.3) returning the bone age evaluation result corresponding to the bone age slice of the subject according to the category obtained in the step (3.2).
the invention also provides a full-automatic bone age assessment device based on the convolutional neural network, which comprises the following components:
The standardized preprocessing module is used for carrying out standardized preprocessing on the wrist bone X-ray film in the database to prepare a training set;
the convolutional neural network training module is used for training a convolutional neural network;
And the bone age evaluation module is used for carrying out data processing on the new bone age tablets by using the trained convolutional neural network to obtain a bone age evaluation result.
The invention also provides a full-automatic bone age assessment device based on the convolutional neural network, which comprises a memory and a processor;
the memory for storing a computer program;
The processor, when executing the computer program, is capable of implementing a fully automatic bone age assessment method based on a convolutional neural network as described above.
the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, is capable of implementing a fully automatic bone age assessment method based on a convolutional neural network as described above.
Compared with the prior art, the invention has the beneficial effects that:
1. The invention estimates the bone age by means of the convolutional neural network, and can automatically realize the derivation of the estimation result.
2. the method can overcome the defect that the key characteristics are lost due to the fact that the wrist bone image is subjected to over-segmentation by a full-automatic segmentation method in the prior art, and the accuracy of an automatic evaluation result within 6 months of error reaches 80%, and the accuracy within 1 year of error reaches 95%, so that the method meets the acceptable requirements of clinical application.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a raw image of a bone age plate for use in the present invention;
FIG. 3 is a schematic diagram of a standardized pre-processing procedure for bone age tablets;
FIG. 4 is a flow chart of the present invention for implementing bone age result output using a deep convolutional neural network.
Detailed Description
it should be noted that the present invention relates to a database technology, which is an application of a computer technology in the field of medical image processing. In the implementation process of the invention, the application of a plurality of software functional modules is involved. The applicant believes that it is fully possible for one skilled in the art to utilize the software programming skills in his or her own practice to implement the invention, as well as to properly understand the principles and objectives of the invention, in conjunction with the prior art, after a perusal of this application. The aforementioned software functional modules include but are not limited to: the standardized preprocessing module, the convolutional neural network training module, the bone age assessment module and the like belong to the scope mentioned in the present application document, and the applicant does not list the modules one by one.
the memory, the processor and the computer readable storage medium are all hardware devices in the computer industry, and the invention has no special requirement on the hardware devices. Except for the specifically described contents, the construction method and the training method of the convolutional neural network can adopt the conventional mode in the field, so the details are not repeated.
the invention is described in further detail below with reference to the following detailed description and accompanying drawings:
The method is used for evaluating the bone age according to bone age tablets and comprises the following specific steps:
firstly, preparing a training set;
Secondly, training a convolutional neural network;
and thirdly, processing the bone age tablet data by using the trained convolutional neural network to obtain the bone age evaluation result.
The first process specifically comprises the following steps:
step A: collecting bone age tablet data of calibrated bone age from a radiology examination database of a hospital, wherein 5500 women and 5000 men in the female patients automatically keep left-hand positive tablets according to the bone age tablet conditions, and unify the bright foreground and the dark background;
and B: b, down-sampling the data collected in the step A into data with the size of 512 multiplied by 512, and then realizing the segmentation and extraction of hands by a full-automatic segmentation method;
and C: and B, according to the data labeling condition in the bone age tablets, calibrating the category of the data in the step B by taking 3 months as a step length according to the calibrated bone age. For example, if the data selected is for a 2-18 year old adolescent child, the bone age correspondence category designated F9Y6M (female with a bone age of 9 years and 6 months) is 30.
The second process specifically comprises the following steps:
Step D: and randomly selecting 90% of data from the wrist bone data which is prepared in the first process and is subjected to standardized preprocessing according to each category as a training set, and performing data augmentation on the training set to increase the data volume to 5 times of the original data volume. The data augmentation methods adopted mainly include but are not limited to: the scale transformation of the foreground image in a certain range, the displacement of a certain degree, the random rotation in a certain angle range and the organic combination of the three transformations;
Step E: and designing the structure of the convolutional neural network. The convolutional neural network is alternately realized by a plurality of convolutional layers, a feature extraction block and a pooling layer, and a loss function value is reduced by an Adam efficient optimization algorithm;
step F: training various parameters in the convolutional neural network by using a training set, referring to the sex of the testee, inputting the training set prepared in the step D and the image calibration category into the convolutional neural network designed in the step E for training, reducing a loss function value and updating the network weight parameter by using an Adam efficient optimization algorithm, and obtaining a learned network weight parameter after a plurality of training;
the third process specifically comprises the following steps:
step G: selecting data needing bone age assessment, carrying out standardized pretreatment on the left-hand orthostatic bone age tablets needing bone age assessment according to the method in the step B, sampling the sizes of the left-hand orthostatic bone age tablets into 512 x 512, and taking the adjusted data as a test set;
step H: randomly selecting data with the size of 512 multiplied by 512 from the test set, taking the image data as input in a convolutional neural network, applying network weight parameters obtained by training and learning of a corresponding sex training set through iteration of the convolutional neural network, and outputting corresponding categories of bone age patch data;
Step I: and D, returning the bone age evaluation result corresponding to the bone age slice of the subject according to the category obtained in the step H.
FIG. 1 is a schematic flow chart of the bone age assessment method of the present invention.
fig. 2 is data of an example bone age slice of female subject 9Y 6M.
fig. 3 is a process of the full-automatic standardized preprocessing of fig. 2, and it can be seen that since the bone age tablet of fig. 2 has a left hand and a right hand, and the left hand is on the right side, the present invention determines that the left hand is on the right side by means of automatic identification, extracts the region where the left hand is on the right side by means of a full-automatic method and performs mirror image transformation, finally retains the left-hand positive tablet, and then extracts the left hand by means of a full-automatic segmentation method, and unifies the image size to 512 × 512.
Fig. 4 is a schematic diagram of the deep convolutional neural network structure of the present invention, which is to perform N consecutive feature extractions, convolutions and pooling on bone age slices of a subject to obtain corresponding classifications, thereby outputting corresponding bone ages. The image obtained in fig. 3 was input to the present deep convolutional neural network, and the final output category was 30, and the bone age was returned to 9Y6M according to the classification method.
Based on the above full-automatic bone age assessment method, the invention also provides a corresponding device and a computer readable storage medium, specifically:
1. there is provided a fully automated bone age assessment device comprising:
The standardized preprocessing module is used for carrying out standardized preprocessing on the wrist bone X-ray film in the database to prepare a training set; the convolutional neural network training module is used for training a convolutional neural network; and the bone age evaluation module is used for carrying out data processing on the new bone age tablets by using the trained convolutional neural network to obtain a bone age evaluation result.
2. The fully-automatic bone age assessment device based on the convolutional neural network comprises a memory and a processor; the memory for storing a computer program; the processor is configured to, when executing the computer program, enable a fully automatic bone age assessment method based on a convolutional neural network as described above.
3. there is provided a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, is able to carry out the fully automatic bone age assessment method based on a convolutional neural network as described above.
In addition, the applicant needs to emphasize that the technical scheme of the invention can only be used as reference test data of the growth and development diseases of the teenagers in medical practice activities, but cannot be directly used for judging whether the disease exists in the subject, and even cannot be used as a disease treatment means for the subject. Therefore, the present invention does not have the purpose of diagnosis or treatment of diseases.
Finally, it should be noted that the above-mentioned list is only a specific embodiment of the present invention. It is obvious that the present invention is not limited to the above embodiments, but many variations are possible. All modifications which can be derived or suggested by a person skilled in the art from the disclosure of the present invention are to be considered within the scope of the invention.
Claims (8)
1. a full-automatic bone age assessment method based on a convolutional neural network is characterized by comprising the following steps:
(1) Carrying out standardized preprocessing by using the wrist bone X-ray film in a database to prepare a training set;
(2) Constructing and training a convolutional neural network;
(3) And carrying out data processing on the new bone age tablets by using the trained convolutional neural network to obtain a bone age evaluation result.
2. The method according to claim 1, characterized in that said step (1) comprises in particular:
(1.1) collecting bone age tablets with calibrated bone age from a radiology examination database of a hospital, selecting left-hand positive tablets with clear images and complete data, and uniformly processing the left-hand positive tablets to show bright foreground and dark background;
(1.2) carrying out image digital sampling on the collected bone age tablets, wherein the size of the image is 512 x 512, and then realizing segmentation and extraction of hand images by a full-automatic segmentation method;
(1.3) according to the labeled bone age corresponding to the bone age tablets, calibrating the category of the image data obtained by sampling according to the calibrated bone age by taking m months as a step length, wherein m is more than or equal to 1 and less than or equal to 6.
3. the method according to claim 1, wherein the step (2) comprises in particular:
(2.1) randomly selecting 90% from the preprocessed wrist bone data as a training set, and performing data augmentation on the training set;
(2.2) constructing a deep convolutional neural network for learning training; the convolutional neural network is alternately realized by a plurality of convolutional layers, a feature extraction block and a pooling layer;
(2.3) inputting the training set prepared in the step (2.1) into the convolutional neural network in the step (2.2), and training parameters in the convolutional neural network; and reducing the loss function value and updating the network weight parameter through an Adam optimization algorithm, and obtaining the learned network weight parameter after a plurality of times of training.
4. the method according to claim 1, wherein in the training in step (2.3), classification is performed according to subject gender in bone age patch calibration data.
5. the method according to claim 1, wherein the step (3) comprises in particular:
(3.1) carrying out standardized pretreatment on the bone age slices of the testee to obtain a clear and complete left-hand positive slice image with the size of 512 multiplied by 512, and processing the left-hand positive slice image to show bright foreground and dark background;
(3.2) inputting the image data obtained in the step (3.1) into a trained convolutional neural network, applying a training set corresponding to the sex of the testee to train learned network weight parameters through iteration of the convolutional neural network, and outputting data corresponding to the category;
and (3.3) returning the bone age evaluation result corresponding to the bone age slice of the subject according to the category obtained in the step (3.2).
6. A fully automatic bone age assessment device based on a convolutional neural network, comprising:
The standardized preprocessing module is used for carrying out standardized preprocessing on the wrist bone X-ray film in the database to prepare a training set;
The convolutional neural network training module is used for training a convolutional neural network;
and the bone age evaluation module is used for carrying out data processing on the new bone age tablets by using the trained convolutional neural network to obtain a bone age evaluation result.
7. A full-automatic bone age assessment device based on a convolutional neural network is characterized by comprising a memory and a processor;
The memory for storing a computer program;
The processor, when executing the computer program, is capable of implementing a fully automatic bone age assessment method based on a convolutional neural network as claimed in any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, is capable of carrying out a fully automatic convolutional neural network-based bone age assessment method according to any one of claims 1 to 5.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111553412A (en) * | 2020-04-27 | 2020-08-18 | 广州市妇女儿童医疗中心（广州市妇幼保健院、广州市儿童医院、广州市妇婴医院、广州市妇幼保健计划生育服务中心） | Method, device and equipment for training precocious puberty classification model |
| CN113112446A (en) * | 2020-03-05 | 2021-07-13 | 成都理工大学 | Tunnel surrounding rock level intelligent judgment method based on residual convolutional neural network |
| CN113362292A (en) * | 2021-05-27 | 2021-09-07 | 重庆邮电大学 | Bone age assessment method and system based on programmable logic gate array |
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| CN107767376A (en) * | 2017-11-02 | 2018-03-06 | 西安邮电大学 | X-ray film stone age Forecasting Methodology and system based on deep learning |
| CN108968991A (en) * | 2018-05-08 | 2018-12-11 | 平安科技（深圳）有限公司 | Hand bone X-ray bone age assessment method, apparatus, computer equipment and storage medium |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107767376A (en) * | 2017-11-02 | 2018-03-06 | 西安邮电大学 | X-ray film stone age Forecasting Methodology and system based on deep learning |
| CN108968991A (en) * | 2018-05-08 | 2018-12-11 | 平安科技（深圳）有限公司 | Hand bone X-ray bone age assessment method, apparatus, computer equipment and storage medium |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113112446A (en) * | 2020-03-05 | 2021-07-13 | 成都理工大学 | Tunnel surrounding rock level intelligent judgment method based on residual convolutional neural network |
| CN111553412A (en) * | 2020-04-27 | 2020-08-18 | 广州市妇女儿童医疗中心（广州市妇幼保健院、广州市儿童医院、广州市妇婴医院、广州市妇幼保健计划生育服务中心） | Method, device and equipment for training precocious puberty classification model |
| CN113362292A (en) * | 2021-05-27 | 2021-09-07 | 重庆邮电大学 | Bone age assessment method and system based on programmable logic gate array |
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