CN109223001A - Hyperactivity prediction technique, system, storage medium and equipment based on nuclear magnetic resonance - Google Patents
Hyperactivity prediction technique, system, storage medium and equipment based on nuclear magnetic resonance Download PDFInfo
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- CN109223001A CN109223001A CN201810983942.4A CN201810983942A CN109223001A CN 109223001 A CN109223001 A CN 109223001A CN 201810983942 A CN201810983942 A CN 201810983942A CN 109223001 A CN109223001 A CN 109223001A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/168—Evaluating attention deficit, hyperactivity
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
- A61B5/004—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
- A61B5/0042—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part for the brain
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
Abstract
The hyperactivity prediction technique based on nuclear magnetic resonance that the invention discloses a kind of, comprising: obtain cranium brain nuclear magnetic resonance image to be diagnosed；The cranium brain nuclear magnetic resonance image to be diagnosed is handled, ectocinerea image to be diagnosed is obtained；Hyperactivity is predicted according to the ectocinerea image to be diagnosed based on hyperactivity prediction model trained in advance.Correspondingly, the invention also discloses a kind of hyperactivity forecasting system, computer readable storage medium and terminal device based on nuclear magnetic resonance.Prediction to hyperactivity can be realized using technical solution of the present invention, and improve predictablity rate.
Description
Technical field
The present invention relates to medicine technology field more particularly to a kind of hyperactivity prediction technique based on nuclear magnetic resonance, system,
Computer readable storage medium and terminal device.
Background technique
Hyperactivity be also known as attention deficit hyperactivity disorder (Attention deficit hyperactivity disorder,
ADHD), the childhood of being common a kind of mental handicape, be mainly shown as and do not collect with age and the disproportionate attention of developmental level
In, notice that the time is of short duration, hyperactivity hyperkinesia and impulsion etc., and be often accompanied by the symptoms such as difficulty of learning, cognitive disorder.Domestic external survey
It was found that the illness rate of hyperactivity is 3%~7%, men and women's ratio is 4~9:1.
Hyperactivity onset before learning, is in chronic process, not only influences the school of children, family and lives outside school, Er Qierong
It easily leads to that the lasting difficulty of learning of children, behavioral problem and self-esteem are low, and such patient keeps on unpleasant terms with people, cannot such as obtain in time
Treatment, some patientss still have symptom after growing up, hence it is evident that school work, physical and mental health and the sociability for influencing patient, therefore, to more
The prediction of dynamic disease has very important significance.
Summary of the invention
The technical problem to be solved by the embodiment of the invention is that providing a kind of hyperactivity prediction side based on nuclear magnetic resonance
Method, system, computer readable storage medium and terminal device can be realized the prediction to hyperactivity, and it is accurate to improve prediction
Rate.
In order to solve the above-mentioned technical problem, the embodiment of the invention provides a kind of hyperactivity prediction side based on nuclear magnetic resonance
Method, comprising:
Obtain cranium brain nuclear magnetic resonance image to be diagnosed；
The cranium brain nuclear magnetic resonance image to be diagnosed is handled, ectocinerea image to be diagnosed is obtained；
Hyperactivity is predicted according to the ectocinerea image to be diagnosed based on hyperactivity prediction model trained in advance.
Further, the hyperactivity prediction model is the model for having merged convolutional neural networks and fully-connected network；Institute
Hyperactivity prediction model is stated including at least activation primitive, convolutional network layer, pond layer and fully connected network network layers.
Further, the method is trained the hyperactivity prediction model by following steps:
The cranium brain nuclear magnetic resonance image of hyperactivity patient and the cranium brain nuclear magnetic resonance image of normal person are obtained respectively；
The cranium brain nuclear magnetic resonance image of cranium brain nuclear magnetic resonance image and the normal person to the hyperactivity patient carries out
Processing, it is corresponding to obtain hyperactivity patient ectocinerea image and normal brain grey matter image；
Mould is predicted to the hyperactivity according to the hyperactivity patient ectocinerea image and the normal brain grey matter image
Type is trained and tests.
Further, the method also includes:
The penalty values of prediction are obtained according to test result and actual result；
The hyperactivity prediction model is optimized according to the penalty values.
Further, it is described based on hyperactivity prediction model trained in advance ectocinerea image to be diagnosed according to more
Dynamic disease is predicted, is specifically included:
Feature extraction is carried out according to the ectocinerea image to be diagnosed based on the convolutional network layer, obtains characteristic pattern；
The characteristic pattern is compressed based on the pond layer, obtains compressed characteristic pattern；
Conversion process is carried out to the compressed characteristic pattern based on the activation primitive；
The result of conversion process is integrated based on the fully connected network network layers, obtains prediction result.
Further, described to obtain cranium brain nuclear magnetic resonance image to be diagnosed, it specifically includes:
The cranium brain nuclear magnetic resonance image to be diagnosed is obtained by nuclear magnetic resonane scanne.
Further, described that the cranium brain nuclear magnetic resonance image to be diagnosed is handled, obtain ectocinerea to be diagnosed
Image specifically includes:
Skull removing, image segmentation, non-linear registration, figure are at least carried out to the cranium brain nuclear magnetic resonance image to be diagnosed
As standardization and smoothing processing, the ectocinerea image to be diagnosed is obtained.
In order to solve the above-mentioned technical problem, the hyperactivity prediction based on nuclear magnetic resonance that the embodiment of the invention also provides a kind of
System, comprising:
Image collection module, for obtaining cranium brain nuclear magnetic resonance image to be diagnosed；
Image processing module obtains brain to be diagnosed for handling the cranium brain nuclear magnetic resonance image to be diagnosed
Grey matter image；And
Prediction module, for based on hyperactivity prediction model trained in advance ectocinerea image to be diagnosed according to more
Dynamic disease is predicted.
The embodiment of the invention also provides a kind of computer readable storage medium, the computer readable storage medium includes
The computer program of storage；Wherein, where the computer program controls the computer readable storage medium at runtime
Equipment executes the hyperactivity prediction technique described in any of the above embodiments based on nuclear magnetic resonance.
The embodiment of the invention also provides a kind of terminal device, including processor, memory and it is stored in the storage
In device and it is configured as the computer program executed by the processor, the processor is real when executing the computer program
The existing hyperactivity prediction technique described in any of the above embodiments based on nuclear magnetic resonance.
Compared with prior art, the hyperactivity prediction technique that the embodiment of the invention provides a kind of based on nuclear magnetic resonance is
System, computer readable storage medium and terminal device, by obtaining cranium brain nuclear magnetic resonance image to be diagnosed, and treat diagnosis
Cranium brain nuclear magnetic resonance image is handled, and ectocinerea image to be diagnosed is obtained, based on hyperactivity prediction model root trained in advance
Hyperactivity is predicted according to ectocinerea image to be diagnosed, can be realized the prediction to hyperactivity, and improve predictablity rate.
Detailed description of the invention
Fig. 1 is a kind of stream of a preferred embodiment of hyperactivity prediction technique based on nuclear magnetic resonance provided by the invention
Cheng Tu；
Fig. 2 is a kind of detailed process of a preferred embodiment of hyperactivity prediction model training method provided by the invention
Figure；
Fig. 3 is the schematic diagram of ROC curve provided in an embodiment of the present invention；
Fig. 4 is that one of the step S13 of a kind of hyperactivity prediction technique based on nuclear magnetic resonance provided by the invention is preferred
The specific flow chart of embodiment；
Fig. 5 is a kind of structural schematic diagram of a preferred embodiment of hyperactivity prediction model provided by the invention；
Fig. 6 is a kind of knot of a preferred embodiment of hyperactivity forecasting system based on nuclear magnetic resonance provided by the invention
Structure block diagram；
Fig. 7 is a kind of structural block diagram of a preferred embodiment of terminal device provided by the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained all without creative efforts
Other embodiments shall fall within the protection scope of the present invention.
It is shown in Figure 1, it is that one of a kind of hyperactivity prediction technique based on nuclear magnetic resonance provided by the invention is preferred
The flow chart of embodiment, including step S11 to step S13:
Step S11, cranium brain nuclear magnetic resonance image to be diagnosed is obtained；
Step S12, the cranium brain nuclear magnetic resonance image to be diagnosed is handled, obtains ectocinerea image to be diagnosed；
Step S13, based on hyperactivity prediction model trained in advance according to the ectocinerea image to be diagnosed to hyperactivity
It is predicted.
Specifically, obtaining cranium brain nuclear magnetic resonance image to be diagnosed, and to the cranium brain nuclear magnetic resonance figures to be diagnosed of acquisition
As carrying out respective handling, so that the ectocinerea image to be diagnosed for being suitable for hyperactivity prediction model is obtained, it will ectocinerea be diagnosed
Image is input to the prediction that hyperactivity disease is carried out in the hyperactivity prediction model that training is completed in advance.
After the cranium brain nuclear magnetic resonance image for treating diagnosis is handled, tractography picture and brain ash can be accordingly obtained
Matter image, and hyperactivity disease is predicted according to ectocinerea image, prediction result ratio obtained is according to tractography picture
The prediction result predicted hyperactivity disease is more accurate, and therefore, the embodiment of the present invention is using ectocinerea image to mostly dynamic
Disease is predicted, and the prediction result output finally obtained is a probability value (between 0%~100%), table
Show that patient suffers from the probability of hyperactivity disease.
It should be noted that the cranium brain nuclear magnetic resonance image in any one embodiment of the invention is cranium brain nuclear magnetic resonance
3D structural images.
It should be added that nuclear magnetic resonance is the atomic nucleus that magnetic moment is not zero, spin energy level under external magnetic field
Zeeman splitting, the physical process of the radio-frequency radiation of RESONANCE ABSORPTION certain frequency occurs.Magnetic resonance imaging (Nuclear
Magnetic Resonance Imaging, NMRI) be also referred to as magnetic resonance imaging (Magnetic Resonance Imaging,
It MRI), is using nuclear magnetic resonance principle, the decaying different in different structure environment inside the substance according to the energy discharged is led to
It crosses additional gradient magnetic and detects launched electromagnetic wave, it can be learnt that the nuclear position of this object and type are constituted, according to
This can be depicted as the structural images of interior of articles.Cranium brain nuclear magnetic resonance figures seem to be applied RF pulse-to-pulse according to brain different tissues
It is formed by image after punching, with the different relaxation times to distinguish the knot of tissue such as white matter of brain, ectocinerea, cerebrospinal fluid
Structure, in clinical medicine and scientific research field, most common institutional framework is white matter of brain and ectocinerea, both institutional frameworks are to constitute
The main component of human brain.
A kind of hyperactivity prediction technique based on nuclear magnetic resonance, passes through the follow-up to acquisition provided by the embodiment of the present invention
Disconnected cranium brain nuclear magnetic resonance image carries out respective handling, to obtain the ectocinerea figure to be diagnosed for being suitable for hyperactivity prediction model
Picture, and by the hyperactivity prediction model after diagnosing ectocinerea image and being input to training, it can be realized to hyperactivity disease
Prediction, and hyperactivity disease is predicted according to ectocinerea image, improve predictablity rate.
In a further advantageous embodiment, the hyperactivity prediction model is to have merged convolutional neural networks and fully connected network
The model of network；The hyperactivity prediction model includes at least activation primitive, convolutional network layer, pond layer and fully connected network network layers.
Specifically, hyperactivity prediction model used by the embodiment of the present invention is a kind of prediction based on deep learning algorithm
Model, preferably 3D convolutional neural networks model (3D-CNN), are made of, specific structure convolutional neural networks and fully-connected network
At including at least activation primitive, convolutional network layer, pond layer and fully connected network network layers.
Preferably, the hyperactivity prediction model in the embodiment of the present invention includes at least level 2 volume product network layer, 2 layers of pond layer
With 4 layers of fully connected network network layers, and there is before each layer of convolutional network layer an activation primitive be used to judge, in fully connected network
The output layer that is followed by of network layers is used to export prediction result.
It should be added that:
(1) cardinal principle of neural network is to imitate the working principle of cerebral neuron, several neurons are connected into
The signal of input is centainly handled (may be cumulative, filtering or other any modes) by network, each neuron, then will
Input that signal spreads out of as target nerve member that treated, passes through the extensive repetition of single neuron simple rule, shape
At a complication system, it is finally reached the purpose of nonlinear prediction.
(2) in layered structure, each node of preceding layer can connect fully-connected network with each node of later layer
It connects, network structure in total is the quantity of the cartesian product of whole nodes；The embodiment of the present invention uses 4 layers of fully-connected network, often
One layer is 2048 nodes, 512 nodes, 256 nodes and 128 nodes respectively.
(3) convolutional network needs first to be arranged the convolution rule of image, generally uses matrix multiplication as convolution rule.With this reality
For applying the hyperactivity prediction model in example, (3D image is 3 rank tensors) uses 3 rank tensors as volume on an image tensor
Product core, is slided, the convolution value of convolution kernel and image is as a result on 3D image；Each volume machine core is sliding on 3D image
It can be corresponded to after the completion of dynamic and generate a characteristic pattern, characteristic pattern is equally 3D tensor；Then characteristic pattern is subjected to pondization operation, obtained
The characteristic pattern of compressed version (characteristic pattern is the corresponding Feature Mapping of convolution kernel)；Characteristic pattern is may be selected behind pond using activation letter
Number carries out conversion process, or is directly entered the training of next layer of convolutional network layer；Wherein, each layer of convolutional network layer is by convolution
Core forms, and the number of the convolution kernel of each layer of convolutional network layer in the embodiment of the present invention is adjustable parameter, default setting 128
× 64；Weight in convolutional network layer is approximately convolution kernel.
(4) convolution kernel: for carrying out the feature unit of convolutional calculation to image, general dimension is identical with target dimension, volume
The size of product core is adjustable ginseng item, and default setting 8*8*8, i.e. length, width and height are 8 pixels.The generation of convolution kernel: machine learning
There are many kinds of the generating modes of convolution kernel, such as artificial customization, random generation, autocoder generation and PAC principal component analysis
Method generation etc..The embodiment of the present invention uses autocoder generating mode, the principle of autocoder be simply interpreted as by
Image is input in trainable neural network, by the feature extraction of network, then is exported one and is inputted identical image, with
Precision is continuously improved by way of backpropagation as penalty values in mean square deviation between input and output, this nerve net
Network as a result, being exactly convolution kernel.By the convolution kernel that autocoder generates can to have in training one it is relatively good
Convolution kernel substantially increases trained precision problem.
(5) autocoder: being divided into two stages of coding and decoding, is the effective means for extracting high dimensional data feature.It compiles
The code stage carries out feature extraction to target data by convolutional network, and convolution kernel at this time is generated by the mode of random initializtion；
After coding stage, one layer of fully-connected network decoding is connect, i.e., full connection is carried out by convolution kernel and generates a data, comparison generates
Data and input data difference, generate a mean square deviation penalty values, be trained, obtained as mesh using optimizing this penalty values
Self-encoding encoder as a result, i.e. classification based training when convolution kernel.
(6) pond: to the compression processing of characteristic pattern, the mode in pond has maximum value pond, minimum value pondization and mean value pond
Change, the embodiment of the present invention uses mean value pond.
(7) activation primitive: for activating the state of node, it is used in the embodiment of the present invention have " relu ", " tanh ",
" liner " three kinds of activation primitives, wherein " relu " and " tanh " is nonlinear activation function.
It as shown in connection with fig. 2, is an a kind of preferred embodiment of hyperactivity prediction model training method provided by the invention
Specific flow chart, the method is trained the hyperactivity prediction model by step S21 to step S23:
Step S21, the cranium brain nuclear magnetic resonance image of hyperactivity patient and the cranium brain nuclear magnetic resonance figures of normal person are obtained respectively
Picture；
Step S22, the cranium brain nuclear magnetic resonance of the cranium brain nuclear magnetic resonance image to the hyperactivity patient and the normal person
Image is handled, corresponding to obtain hyperactivity patient ectocinerea image and normal brain grey matter image；
Step S23, according to the hyperactivity patient ectocinerea image and the normal brain grey matter image to described mostly dynamic
Disease prediction model is trained and tests.
Specifically, obtaining the cranium brain nuclear magnetic resonance image of the hyperactivity patient of preset quantity, and the hyperactivity of acquisition is suffered from
The cranium brain nuclear magnetic resonance image of person carries out respective handling, to obtain the brain for being suitable for the hyperactivity patient of hyperactivity prediction model
The ectocinerea image of hyperactivity patient is input in hyperactivity prediction model and is trained and tests, similarly, obtains by grey matter image
The cranium brain nuclear magnetic resonance image of the normal person of preset quantity is taken, and phase is carried out to the cranium brain nuclear magnetic resonance image of the normal person of acquisition
It should handle, so that the ectocinerea image for being suitable for the normal person of hyperactivity prediction model is obtained, by the ectocinerea image of normal person
It is input in hyperactivity prediction model and is trained and tests, the hyperactivity prediction model after training is distinguished mostly dynamic
The ectocinerea image of disease patient and the ectocinerea image of normal person, so as to go out hyperactivity according to ectocinerea image automatic Prediction
Patient and normal person.
It should be noted that when being trained to hyperactivity prediction model, the cranium brain core of used hyperactivity patient
The quantity of the cranium brain nuclear magnetic resonance image of magnetic resonance image and normal person is more, and the prediction of the hyperactivity prediction model after training is quasi-
True rate is higher.
It should be added that differentiating two parameters of test result service precision and AUC, wherein precision is specifically criticized
Really prediction number accounts for the ratio of overall test number；AUC refers specifically to ROC curve (receiver operating
Characteristic curve, recipient's operating characteristic curve) under area, between 0.5~1, ROC curve shows value
It is intended to as shown in figure 3, horizontal axis is false positive example rate, the longitudinal axis is real example rate, when judging the quality of a binary classification algorithm, AUC
Good reference role can be played, for the value of AUC closer to 1, algorithm classification effect is better, it is meant that correctly judges ratio
It is higher.
In another preferred embodiment, the method also includes:
The penalty values of prediction are obtained according to test result and actual result；
The hyperactivity prediction model is optimized according to the penalty values.
Specifically, when hyperactivity prediction model is trained and is tested according to different ectocinerea images, each
Ectocinerea image has an actual result (to indicate that the ectocinerea image is ectocinerea image or the normal person of hyperactivity patient
Ectocinerea image) and the corresponding test result obtained, obtained according to the corresponding test result of ectocinerea image and actual result
The penalty values of prediction, so as to be optimized according to penalty values to hyperactivity prediction model, to improve hyperactivity prediction model
Precision.
It should be noted that the embodiment of the present invention is using the mean square deviation of prediction result collection and actual result collection as prediction
Penalty values, it is optimal the result is that mean square deviation is 0.Regard each layer network as a function about input image data,
Variance equally can be regarded as a function of network parameter, by asking mean square deviation functional minimum value that can make the standard of prediction
True rate reaches highest, and mean square deviation function is minimized corresponding parameter then and is the parameter of target network, and solution procedure can pass through
The mode of gradient decline carries out, this process is referred to as backpropagation, by backpropagation, so that penalty values reach minimum to obtain
To optimal network parameter.
After the decline of constantly gradient, the precision of hyperactivity prediction model can be just improved, gradient index meaning
On gradient, regard each layer network as a function, penalty values can regard a function about network parameter, network here as
Parameter includes the weight of fully connected network network layers, the convolution kernel of biasing and convolutional network layer, biasing.It, must be first if function is most worth
Extreme value is taken, the purpose of gradient decline is the value of network parameter when obtaining each extreme point calculating penalty values minimum.
It is one of the step S13 of a kind of hyperactivity prediction technique based on nuclear magnetic resonance provided by the invention referring to fig. 4
The specific flow chart of preferred embodiment, it is described based on hyperactivity prediction model trained in advance ectocinerea figure to be diagnosed according to
Picture predicts hyperactivity, specifically includes step S1301 to step S1304:
Step S1301, feature extraction is carried out according to the ectocinerea image to be diagnosed based on the convolutional network layer, obtained
Characteristic pattern；
Step S1302, the characteristic pattern is compressed based on the pond layer, obtains compressed characteristic pattern；
Step S1303, conversion process is carried out to the compressed characteristic pattern based on the activation primitive；
Step S1304, the result of conversion process is integrated based on the fully connected network network layers, obtains prediction result.
Specifically, hyperactivity predicts mould by the hyperactivity prediction model after diagnosing ectocinerea image and being input to training
Type treats diagnosis ectocinerea image by the convolution kernel of convolutional network layer and carries out feature extraction, obtains ectocinerea image pair to be diagnosed
The characteristic pattern answered is compressed by characteristic pattern of the pond layer to acquisition, corresponding to obtain compressed characteristic pattern, by activating letter
The compressed characteristic pattern of several pairs of acquisitions carries out conversion process, then cumulative to activation primitive by the judgement of fully connected network network layers
Conversion process result is integrated, and the prediction result of hyperactivity is finally obtained, to realize the prediction to hyperactivity.
It should be understood that passing through the hyperactivity prediction model pair after training according to wait diagnose ectocinerea image in the present embodiment
The basic principle that hyperactivity is predicted is trained and tests to hyperactivity prediction model from according to different ectocinerea images
Basic principle it is identical.
It as shown in connection with fig. 5, is that the structure of a preferred embodiment of hyperactivity prediction model provided by the invention a kind of is shown
It is intended to, each layer of convolutional network layer is all made of several convolution kernels, and has an activation before each layer of convolutional network layer
Function；Convolution kernel in convolutional network layer 1 carries out feature extraction to the MRI image received, obtains corresponding characteristic pattern,
In, the corresponding characteristic pattern of each convolution kernel, the characteristic pattern of 1 pair of pond layer acquisition compresses, corresponding to obtain compressed spy
Sign figure, characteristic pattern carries out conversion process using activation primitive behind pond, and is carried out by convolutional network layer 2 and pond layer 2
It handles again, obtains prediction result finally by fully connected network network layers, and prediction result is exported by output layer.
It is described to obtain cranium brain nuclear magnetic resonance image to be diagnosed in another preferred embodiment, it specifically includes:
The cranium brain nuclear magnetic resonance image to be diagnosed is obtained by nuclear magnetic resonane scanne.
It should be understood that the cranium brain nuclear magnetic resonance image of people can be obtained by nuclear magnetic resonane scanne.
It should be noted that obtaining the method and training hyperactivity of cranium brain nuclear magnetic resonance image to be diagnosed in the present embodiment
The method phase of the cranium brain nuclear magnetic resonance image of hyperactivity patient and the cranium brain nuclear magnetic resonance image of normal person is obtained when prediction model
Together.
Currently, the nuclear magnetic resonane scanne being most widely used on the market has the types such as Siemens, Philip and GE, it is mostly dynamic
The cranium brain nuclear magnetic resonance image of disease patient and normal person are mainly derived from these types of nuclear magnetic resonane scanne, what every kind of type acquired
Cranium brain nuclear magnetic resonance image is DICOM format, needs to convert cranium brain nuclear magnetic resonance image after by picture quality screening
The processing of next step is carried out for the file of NIFTI format.In the setting of sweep parameter, thickness be traditionally arranged to be 1mm or
1.2mm, field strength are traditionally arranged to be 1.5T or 3.0T, and resolution ratio is traditionally arranged to be 1*1 or 1.2*1.2.
It is described that the cranium brain nuclear magnetic resonance image to be diagnosed is handled in another preferred embodiment, it obtains
Ectocinerea image to be diagnosed, specifically includes:
Skull removing, image segmentation, non-linear registration, figure are at least carried out to the cranium brain nuclear magnetic resonance image to be diagnosed
As standardization and smoothing processing, the ectocinerea image to be diagnosed is obtained.
Specifically, brain structure image original number to be diagnosed can be obtained according to cranium brain nuclear magnetic resonance image to be diagnosed
According to by carrying out skull removing, image segmentation, non-linear registration, image standardization peace to brain structure original image data
The processing such as sliding, obtains the ectocinerea image to be diagnosed predicted for hyperactivity prediction model.
The analysis software that the cranium brain nuclear magnetic resonance image for treating diagnosis that the present embodiment uses is handled is to be based on
The SPM software package of MATLAB, the software package are absorbed in the processing and analysis of brain phantom data, and associated process steps are established
On the basis of DARTEL instrumental function in SPM software, the processing of traditional nuclear magnetic resonance brain structure original image data is built
Environment and SPM software package in MATLAB exploitation are found, and the embodiment of the present invention will be in SPM software using MATLAB runtime environment
The whole processing steps being related to are integrated into code, automatic to read brain structure original image data and run in order all
Processing step, all treatment processes are run in Cent OS system.
SPM software package based on MATLAB carries out skull separating treatment to cranium brain nuclear magnetic resonance image first, and carries out figure
As segmentation obtains corresponding tractography picture and ectocinerea image, then to the tractography picture and ectocinerea image being partitioned into
Row non-linear registration, finally by after registration tractography picture and ectocinerea image standardization to MNI coordinate space (MNI coordinate
Space is one of most common human brain coordinate-system in the world), and carry out Gaussian smoothing.
The whole process that the embodiment of the present invention handles the cranium brain nuclear magnetic resonance image for treating diagnosis passes through coding
Form, which is realized, to be fully automated, automatic to read brain structure original image data, is then based on the completion of MATLAB runtime environment
Entire treatment process, does not need the MATLAB of commercial version, does not need to be located manually according to each step of DARTEL tool yet
Reason.
It should be noted that obtaining the method and training hyperactivity prediction model of ectocinerea image to be diagnosed in the present embodiment
When obtain hyperactivity patient ectocinerea image it is identical with the method for normal brain grey matter image.
The hyperactivity forecasting system based on nuclear magnetic resonance that the embodiment of the invention also provides a kind of, can be realized any of the above-described
All processes of hyperactivity prediction technique based on nuclear magnetic resonance provided by embodiment, modules, unit in system
Effect and realize technical effect respectively with the hyperactivity prediction technique provided by above-described embodiment based on nuclear magnetic resonance
Effect and the technical effect realized correspond to identical, and which is not described herein again.
It is shown in Figure 6, it is that one of a kind of hyperactivity forecasting system based on nuclear magnetic resonance provided by the invention is preferred
The structural block diagram of embodiment, comprising:
Image collection module 11, for obtaining cranium brain nuclear magnetic resonance image to be diagnosed；
Image processing module 12 is obtained for handling the cranium brain nuclear magnetic resonance image to be diagnosed wait diagnose
Ectocinerea image；And
Prediction module 13, for based on hyperactivity prediction model trained in advance ectocinerea image pair to be diagnosed according to
Hyperactivity is predicted.
Preferably, the hyperactivity prediction model is the model for having merged convolutional neural networks and fully-connected network；It is described
Hyperactivity prediction model includes at least activation primitive, convolutional network layer, pond layer and fully connected network network layers.
Preferably, the system is trained the hyperactivity prediction model by following steps:
The cranium brain nuclear magnetic resonance image of hyperactivity patient and the cranium brain nuclear magnetic resonance image of normal person are obtained respectively；
The cranium brain nuclear magnetic resonance image of cranium brain nuclear magnetic resonance image and the normal person to the hyperactivity patient carries out
Processing, it is corresponding to obtain hyperactivity patient ectocinerea image and normal brain grey matter image；
Mould is predicted to the hyperactivity according to the hyperactivity patient ectocinerea image and the normal brain grey matter image
Type is trained and tests.
Preferably, the step of hyperactivity prediction model being trained further include:
The penalty values of prediction are obtained according to test result and actual result；
The hyperactivity prediction model is optimized according to the penalty values.
Preferably, the prediction module specifically includes:
Feature extraction unit is mentioned for carrying out feature according to the ectocinerea image to be diagnosed based on the convolutional network layer
It takes, obtains characteristic pattern；
Compression unit obtains compressed characteristic pattern for compressing based on the pond layer to the characteristic pattern；
Converting unit, for carrying out conversion process to the compressed characteristic pattern based on the activation primitive；And
Predicting unit obtains prediction knot for integrating based on the fully connected network network layers to the result of conversion process
Fruit.
Preferably, described image obtains module and specifically includes:
Image acquisition unit, for obtaining the cranium brain nuclear magnetic resonance image to be diagnosed by nuclear magnetic resonane scanne.
Preferably, described image processing module specifically includes:
Image processing unit, at least carrying out skull removing, image to the cranium brain nuclear magnetic resonance image to be diagnosed
Segmentation, non-linear registration, image standardization and smoothing processing obtain the ectocinerea image to be diagnosed.
The embodiment of the invention also provides a kind of computer readable storage medium, the computer readable storage medium includes
The computer program of storage；Wherein, where the computer program controls the computer readable storage medium at runtime
Equipment executes the hyperactivity prediction technique described in any of the above-described embodiment based on nuclear magnetic resonance.
It is shown in Figure 7 the embodiment of the invention also provides a kind of terminal device, it is that a kind of terminal provided by the invention is set
The structural block diagram of a standby preferred embodiment, including processor 10, memory 20 and be stored in the memory 20 and
It is configured as the computer program executed by the processor 10, the processor 10 is realized when executing the computer program
Hyperactivity prediction technique described in any of the above-described embodiment based on nuclear magnetic resonance.
Preferably, the computer program can be divided into one or more module/units (such as computer program 1, meter
Calculation machine program 2), one or more of module/units are stored in the memory 20, and by
The processor 10 executes, to complete the present invention.One or more of module/units, which can be, can complete specific function
Series of computation machine program instruction section, the instruction segment is for describing execution of the computer program in the terminal device
Journey.
The processor 10 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc., general processor can be microprocessor or the processor 10 is also possible to any conventional place
Device is managed, the processor 10 is the control centre of the terminal device, utilizes terminal device described in various interfaces and connection
Various pieces.
The memory 20 mainly includes program storage area and data storage area, wherein program storage area can store operation
Application program needed for system, at least one function etc., data storage area can store related data etc..In addition, the memory
20 can be high-speed random access memory, can also be nonvolatile memory, such as plug-in type hard disk, intelligent memory card
(Smart Media Card, SMC), secure digital (Secure Digital, SD) card and flash card (Flash Card) etc., or
The memory 20 is also possible to other volatile solid-state parts.
It should be noted that above-mentioned terminal device may include, but it is not limited only to, processor, memory, those skilled in the art
Member is appreciated that Fig. 7 structural block diagram is only the example of terminal device, does not constitute the restriction to terminal device, may include
Than illustrating more or fewer components, certain components or different components are perhaps combined.
To sum up, a kind of hyperactivity prediction technique based on nuclear magnetic resonance, system, computer provided by the embodiment of the present invention
Readable storage medium storing program for executing and terminal device carry out respective handling by the cranium brain nuclear magnetic resonance image to be diagnosed to acquisition, to obtain
The ectocinerea image to be diagnosed suitable for hyperactivity prediction model is obtained, and will be more after diagnosing ectocinerea image and being input to training
In dynamic disease prediction model, the prediction to hyperactivity disease can be realized, and carry out to hyperactivity disease according to ectocinerea image
Prediction, improves predictablity rate.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations
Also it should be regarded as protection scope of the present invention.
Claims (10)
1. a kind of hyperactivity prediction technique based on nuclear magnetic resonance characterized by comprising
Obtain cranium brain nuclear magnetic resonance image to be diagnosed；
The cranium brain nuclear magnetic resonance image to be diagnosed is handled, ectocinerea image to be diagnosed is obtained；
Hyperactivity is predicted according to the ectocinerea image to be diagnosed based on hyperactivity prediction model trained in advance.
2. the hyperactivity prediction technique based on nuclear magnetic resonance as described in claim 1, which is characterized in that the hyperactivity prediction
Model is the model for having merged convolutional neural networks and fully-connected network；The hyperactivity prediction model includes at least activation letter
Number, convolutional network layer, pond layer and fully connected network network layers.
3. the hyperactivity prediction technique based on nuclear magnetic resonance as claimed in claim 1 or 2, which is characterized in that the method is logical
Following steps are crossed to be trained the hyperactivity prediction model:
The cranium brain nuclear magnetic resonance image of hyperactivity patient and the cranium brain nuclear magnetic resonance image of normal person are obtained respectively；
The cranium brain nuclear magnetic resonance image of cranium brain nuclear magnetic resonance image and the normal person to the hyperactivity patient is handled,
It is corresponding to obtain hyperactivity patient ectocinerea image and normal brain grey matter image；
According to the hyperactivity patient ectocinerea image and the normal brain grey matter image to the hyperactivity prediction model into
Row training and test.
4. the hyperactivity prediction technique based on nuclear magnetic resonance as claimed in claim 3, which is characterized in that the method is also wrapped
It includes:
The penalty values of prediction are obtained according to test result and actual result；
The hyperactivity prediction model is optimized according to the penalty values.
5. the hyperactivity prediction technique based on nuclear magnetic resonance as claimed in claim 2, which is characterized in that described based on instruction in advance
Experienced hyperactivity prediction model predicts hyperactivity according to the ectocinerea image to be diagnosed, and specifically includes:
Feature extraction is carried out according to the ectocinerea image to be diagnosed based on the convolutional network layer, obtains characteristic pattern；
The characteristic pattern is compressed based on the pond layer, obtains compressed characteristic pattern；
Conversion process is carried out to the compressed characteristic pattern based on the activation primitive；
The result of conversion process is integrated based on the fully connected network network layers, obtains prediction result.
6. the hyperactivity prediction technique based on nuclear magnetic resonance as described in claim 1, which is characterized in that described to obtain wait diagnose
Cranium brain nuclear magnetic resonance image, specifically include:
The cranium brain nuclear magnetic resonance image to be diagnosed is obtained by nuclear magnetic resonane scanne.
7. the hyperactivity prediction technique based on nuclear magnetic resonance as described in claim 1, which is characterized in that described to the follow-up
Disconnected cranium brain nuclear magnetic resonance image is handled, and is obtained ectocinerea image to be diagnosed, is specifically included:
Skull removing, image segmentation, non-linear registration, image mark are at least carried out to the cranium brain nuclear magnetic resonance image to be diagnosed
Standardization and smoothing processing obtain the ectocinerea image to be diagnosed.
8. a kind of hyperactivity forecasting system based on nuclear magnetic resonance characterized by comprising
Image collection module, for obtaining cranium brain nuclear magnetic resonance image to be diagnosed；
Image processing module obtains ectocinerea to be diagnosed for handling the cranium brain nuclear magnetic resonance image to be diagnosed
Image；And
Prediction module, for based on hyperactivity prediction model trained in advance ectocinerea image to be diagnosed according to hyperactivity
It is predicted.
9. a kind of computer readable storage medium, which is characterized in that the computer readable storage medium includes the calculating of storage
Machine program；Wherein, the equipment where the computer program controls the computer readable storage medium at runtime executes such as
Hyperactivity prediction technique described in any one of claims 1 to 7 based on nuclear magnetic resonance.
10. a kind of terminal device, which is characterized in that including processor, memory and store in the memory and matched
It is set to the computer program executed by the processor, the processor is realized when executing the computer program as right is wanted
Hyperactivity prediction technique described in asking any one of 1 to 7 based on nuclear magnetic resonance.
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