CN109727227A - A kind of diagnosis of thyroid illness method based on SPECT image - Google Patents
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Abstract
The present invention provides a kind of diagnosis of thyroid illness method based on SPECT image, image classification is carried out using a kind of convolutional neural networks of improved DenseNet network structure, increase parameter influential for weight in the parallel link in Dense block, and the weight of the characteristic pattern of former each layer is made to carry out dynamic adjustment in training, so that network has greater flexibility, classification performance is improved.Embodiment shows that this method can obtain the performance better than other deep learning methods.The present invention can be widely applied to the diagnosis and other image classification problems of thyroid disease.
Description
Technical field
The present invention relates to a kind of diagnosis of thyroid illness methods based on SPECT image.
Background technique
Thyroid gland can produce thyroid hormone, play a crucial role in control human metabolism.First shape
Parathyrine (T4) and trilute (T3) are the two kinds of active thyroid hormones generated by thyroid gland, they have human body
Very big help, generation, body heat regulation and energy production and adjusting including protein.Thyroid disease is endocrine field
The second largest disease[1], serious thyroid disease may cause dead [1-3].
Clinically Diagnosis of Thyroid Diseases commonly because be known as very much, as clinical evaluation, blood test, imageological examination,
Biopsy etc..Wherein image method is a kind of very important thyroid gland diagnostic method, these images mainly include ultrasound, CT,
SPECT etc..Ultrasound is a kind of convenience, imaging method in real time, economic, is usually used in Clinical screening and judges the property of thyroid nodule
Matter.In recent years, the method based on convolutional neural networks also be used to identify Benign And Malignant Nodules of Thyroid Glands [4,5].CT images can be with
For finding the diseases such as thyroid adenoma and cancer, neural network method can be used for region thyreoidea regional partition and volumetric estimate
[6]。
SPECT is a kind of nuclear medicine using conventional gamma camera acquisition image data.SPECT imaging system is by pacifying
The conventional gamma camera composition of one or more on frame, detector can surround patient while collecting these images
Precisely and automatically rotate.It is substantially three-dimensional that the major advantage of SPECT imaging, which is the image generated,.Due to SPECT image
It is able to reflect thyroid function situation, is not changed in thyroid physical aspect, and when function generation obstacle, ultrasound
It can not just detected with CT images, therefore SPECT image can just find in time disease in disease early stage, identify difficult first
It plays an important role in shape gland disease.Clinical assistant diagnosis can reduce doctor's false diagnosis due to caused by the factors such as fatigue,
Carry out the auxiliary diagnosis work based on SPECT image, can be improved the accuracy of clinical diagnosis.
Machine learning is a kind of important aided diagnosis method, has been largely used to the detection and diagnosis based on medical image
In.Supervised learning is a kind of important machine learning method, is learnt by the training sample and corresponding disease label of image
Mathematical function, and any class disease [7-8] judged until the lesion in image or belong to.Main supervised learning algorithm includes
Neural network, support vector machine and deep learning method.Deep learning is a kind of preferable machine learning method of application effect, with
The development of graphics processing unit (GPU), deep learning breakthrough performance is obtained in various medical applications.Convolutional Neural
Network (CNN) is a kind of widely applied deep learning method [9-18] in medical image analysis field.CNN is with 2D or 3D rendering
As input, there are multilayered structure, including pond layer, convolutional layer, RELU layers and full articulamentum etc., there is local sensing, weight
The characteristics of shared and more convolution kernels, to significantly reduce the calculation amount of the quantity of parameters in neural network model.
Classify although existing CNN method is used directly for SPECT image to thyroid disease, realizes thyroid gland
The diagnosis of disease, but these existing methods have that accuracy is low, performance is bad.The present invention is directed to this problem, mentions
A kind of diagnosis of thyroid illness method based on SPECT image out.
Summary of the invention
It is applied to the problem that accuracy is low, performance is bad existing for SPECT image, the present invention for existing CNN method
A kind of diagnosis of thyroid illness method based on SPECT image is proposed, this method uses a kind of improved CNN network structure,
Trainable weight parameter is added in parallel link by the structure, allows the network to the ginseng for learning weight during the training period
Number overcomes the problems, such as that parallel link existing for original network will lead to information redundancy and reduce network performance, to improve knowledge
The accuracy of other method.
DenseNet is a kind of widely applied CNN network structure [18].Its main feature is that alleviating ladder by intensively connection
The problem of degree disappears reinforces feature propagation, reduces parameter amount.In network each layer of input be all the output of all layers of front and
Collection, and the characteristic pattern that this layer is learnt can also be directly transmitted to be used as input for all layers behind.DenseNet utilizes every layer of reduction
Calculation amount and feature multiplexing improve the efficiency of network, by all layers after allowing l layers of input to directly influence,
At l layers, output and input relationship have
yl=Fl([x0,x1,...,xl-1],{Wl}), (1)
Wherein, l indicates the current number of plies, ylIt is the output of this layer；[x0,x1,...,xl-1] be 0,1 ..., in l-1 layers
The characteristic pattern (feature map) of generation, merges (concatenation) with the dimension in channel；FlIndicate non-linear change
It changes, including BN, the latticed forms such as convolution of ReLU and 3x3, WlIndicate FlParameter.
The present invention improves network structure on the basis of DenseNet.Parallel link in DenseNet is with identical
Weight connect all pervious features, but not all pervious feature is all useful, therefore this connection will lead to
Information redundancy and reduction network performance.
The present invention provides a kind of diagnosis of thyroid illness method based on SPECT image, by using convolutional neural networks
Machine learning method classify to SPECT image, achieve the purpose that detection or identification thyroid disease, aforementioned convolution mind
DenseNet network structure or improved DenseNet network structure are used through network, it is characterised in that:
First, aforementioned convolutional neural networks connection has the feature that in each intensive connection dense block module
Trainable weight parameter is added in each parallel link by the inside, and initialization value is set as 1, to will not influence training
Preceding weight, in the forward propagation process, by each layer of feature in network and corresponding multiplied by weight, obtain in this way
The l layers of relationship output and input are
yl=Fl([x0·kl,0,x1·kl,1,...,xl-1·kl,l-1],{Wl}), (2)
Wherein, l indicates the current number of plies, ylIt is the output of this layer；[x0,x1,...,xl-1] be 0,1 ..., in l-1 layers
The characteristic pattern of generation；FlIndicate nonlinear transformation, latticed form including but not limited to below: the convolution of BN, ReLU and 3x3；Wl
Indicate FlParameter；kl,0,kl,1,...,kl,l-1It refers to working as x0,x1,...,xl-1X is determined when being connected to l layers0,x1,...,xl-1
Weight parameter；
Second, it is aforementioned that aforementioned convolutional neural networks have the feature that network learns during the training period in learning process
The parameter of weight, in back-propagation process, the value of aforementioned weight parameter indicates the influence degree of individual features figure, when this is corresponding
Characteristic pattern in classification task comprising more useful information or when playing main, join by the corresponding weight of the individual features figure
Number will be relatively large；And working as this feature figure includes less useful information in classification task, perhaps plays a secondary role or does not rise
When effect, which will be relatively small.
Above-mentioned improvement has the benefit that the feature weight due to each layer is no longer fixed, net in connection
Network has greater flexibility, and has the ability for filtering invalid feature；Meanwhile the pond layer in network is replaced by extension
Convolutional layer, to save the useful information of feature as much as possible.
The present invention utilizes the improved network structure and learning process of DenseNet, feature summation can be overcome to weaken scarce
Point obtains more accurate testing result.With reference to the accompanying drawing, specific implementation example and its advantages are made further
It is bright.
Detailed description of the invention
The improved network structure of Fig. 1
The specific network information of Fig. 2 embodiment
The mean accuracy curve of Fig. 3 difference the number of iterations
The confusion matrix of Fig. 4 distinct methods
Specific embodiment
With reference to the accompanying drawing, to the specific of the diagnostic method of the thyroid disease provided by the invention based on SPECT image
Embodiment is described as follows:
Fig. 1 gives improved network structure, and increase parallel link in intensively connection dense block module can
Training weight parameter.Fig. 2 gives a kind of specific network information of embodiment, and realization of the invention including but not limited to should
The network information.
Web vector graphic deep learning frame PyTorch in the specific embodiment of the invention is realized, is arranged according to Fig. 2 and is connected
Each layer of network, according to the mode of learning of Fig. 1 and formula (2) setting network.The DenseNet121 provided frame is loaded,
Network is trained in advance using transfer learning method ImageNet, and then it is finely adjusted with SPECT image data set.It uses
SGD trains network, momentum 0.9.Each include 5 images in small batches, and each image size is 255 × 255.Initial study
Rate is set as 0.001, and the learning rate of every 5 cycles of training is originally 1/10, and loss function is set as intersecting entropy loss.
The performance of the method for the present invention for further evaluation has carried out identical 10 experiments in the present embodiment, and to knot
Fruit is averaged.The present invention is equipped with two NVIDIA Geforce 1080Ti GPU, an Intel Xeon E5- at one
The work station of 2620 CPU carries out experiment.
Four kinds of common thyroid diseases of SPECT diagnostic imaging that the data set of this embodiment uses, including thyroid gland
Hyperfunction, hypothyroidism, methylene inflammation and Hashimoto's disease.In the SPECT data set, there are 800 width normal thyroids
Image, the image of 650 width hyperthyroidism, the image of 200 width hypothyroidism, the image of 650 width methylene inflammation, and
The image of 650 width bridge this hyperthyroidism.
Illustrate the beneficial effect that embodiment of the invention provides below.The present invention compares more on the same data set
The performance of a network, including DeaveNet121, ReNet101, VGG19 and EnEntuv3；Multiple indexs are also compared, including are divided
Class precision, precision, recall rate, F1 score and confusion matrix.1 be the results are shown in Table to table 6.
1 hyperthyroidism classification indicators of table
| Precision | Recall rate | F1 score | |
| The method of the present invention | 1.00 | 1.00 | 1.00 |
| DenseNet121 | 1.00 | 1.00 | 1.00 |
| ResNet101 | 1.00 | 0.99 | 0.99 |
| InceptionV3 | 1.00 | 1.00 | 1.00 |
| VGG19 | 1.00 | 1.00 | 1.00 |
The classification indicators of 2 hypothyroidism of table
| Precision | Recall rate | F1-score | |
| The method of the present invention | 0.93 | 0.94 | 0.93 |
| DenseNet121 | 0.92 | 0.87 | 0.89 |
| ResNet101 | 0.92 | 0.89 | 0.90 |
| InceptionV3 | 0.91 | 0.87 | 0.89 |
| VGG19 | 0.93 | 0.84 | 0.88 |
3 methylene inflammation classification indicators of table
| Precision | Recall rate | F1-score | |
| The method of the present invention | 0.95 | 0.91 | 0.92 |
| DenseNet121 | 0.88 | 0.92 | 0.90 |
| ResNet101 | 0.89 | 0.93 | 0.91 |
| InceptionV3 | 0.87 | 0.91 | 0.89 |
| VGG19 | 0.85 | 0.93 | 0.89 |
4 Hashimoto thyroiditis classification indicators of table
| Precision | Recall rate | F1-score | |
| The method of the present invention | 1.00 | 1.00 | 1.00 |
| DenseNet121 | 1.00 | 1.00 | 1.00 |
| ResNet101 | 0.99 | 0.99 | 0.99 |
| InceptionV3 | 1.00 | 0.99 | 0.99 |
| VGG19 | 1.00 | 0.99 | 1.00 |
The normal classification indicators of table 5
| Precision | Recall rate | F1-score | |
| The method of the present invention | 1.00 | 1.00 | 1.00 |
| DenseNet121 | 1.00 | 1.00 | 1.00 |
| ResNet101 | 0.99 | 1.00 | 1.00 |
| InceptionV3 | 0.99 | 1.00 | 0.99 |
| VGG19 | 0.99 | 1.00 | 1.00 |
Table 6 is averaged classification indicators
| Average Precision | Average Recall rate | Average F1-score | |
| The method of the present invention | 0.98 | 0.97 | 0.97 |
| DenseNet121 | 0.97 | 0.96 | 0.96 |
| ResNet101 | 0.97 | 0.96 | 0.97 |
| InceptionV3 | 0.96 | 0.96 | 0.96 |
| VGG19 | 0.96 | 0.96 | 0.96 |
The experimental results showed that this method has higher detection and identification accuracy than other convolutional neural networks methods,
Classification performance is also superior to other methods.For hyperthyroidism, Hashimoto thyroiditis and normal is proposed by the present invention
Method and other most methods can obtain good accuracy.For other two classes, due to training sample or sample distribution
Deficiency, all methods have some classification errors.However be compared with other methods, network proposed by the present invention is to SPECT first
Shape gland image has stronger ability in feature extraction, and can make full use of all potential informations in image.Even if therefore two classes
Image has less training data or unreasonable sample distribution, method classification error in all methods proposed by the present invention
Rate is still minimum.
The comparison of the mean accuracy of the different the number of iterations of distinct methods is given in Fig. 3, and method of the invention can be
Optimal precision is obtained under different the number of iterations.This means that the network proposed has excellent general classification performance, without
Dependent on the number of iterations.
Confusion matrix, also known as error matrix are to indicate to carry out classification method in the matrix form in computer learning field
A kind of method of evaluation.The concrete class that each list diagram picture of confusion matrix is classified into, the sum of each column indicate real
Border is classified as the amount of images of classification, and every a line indicates the real property classification of image, and the sum of every a line indicates the category
Image instance quantity.Fig. 4 gives the confusion matrix of distinct methods.Classification error proposed by the present invention is minimum, it is seen that
Its method has the detection accuracy better than other convolutional neural networks methods.
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Claims (1)
1. a kind of diagnosis of thyroid illness method based on SPECT image, by using the machine learning side of convolutional neural networks
Method classifies to SPECT image, achievees the purpose that detection or identification thyroid disease, aforementioned convolutional neural networks use
DenseNet network structure or improved DenseNet network structure, it is characterised in that:
First, aforementioned convolutional neural networks connection has the feature that in each intensive connection dense block module
Trainable weight parameter is added in each parallel link by face, and initialization value is set as 1, thus before will not influence training
Weight, in the forward propagation process, by each layer of feature in network and corresponding multiplied by weight, obtain in this way in l
The relationship that outputs and inputs of layer be
yl=Fl([x0·kl,0,x1·kl,1,...,xl-1·kl,l-1],{Wl}), (2)
Wherein, l indicates the current number of plies, ylIt is the output of this layer；[x0,x1,...,xl-1] be 0,1 ..., generated in l-1 layers
Characteristic pattern；FlIndicate nonlinear transformation, latticed form including but not limited to below: the convolution of BN, ReLU and 3x3；WlIt indicates
FlParameter；kl,0,kl,1,...,kl,l-1It refers to working as x0,x1,...,xl-1X is determined when being connected to l layers0,x1,...,xl-1Power
The parameter of weight；
Second, aforementioned convolutional neural networks have the feature that network learns aforementioned weight during the training period in learning process
Parameter, in back-propagation process, the value of aforementioned weight parameter indicates the influence degree of individual features figure, when the individual features
For figure in classification task comprising more useful information or when playing main, the corresponding weight parameter of the individual features figure will
It is relatively large；And working as this feature figure includes less useful information in classification task, perhaps plays a secondary role or does not work
When, which will be relatively small.
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| CN111340906A (en) * | 2020-02-25 | 2020-06-26 | 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) | SPECT (single photon emission computed tomography) tomographic image reconstruction method, device and equipment combining ART (ART) and UNet algorithm |
| CN112070755A (en) * | 2020-09-14 | 2020-12-11 | 内江师范学院 | New coronary pneumonia image identification method based on combination of deep learning and transfer learning |
| CN112070089A (en) * | 2020-09-23 | 2020-12-11 | 西安交通大学医学院第二附属医院 | Ultrasonic image-based intelligent diagnosis method and system for diffuse thyroid diseases |
| CN114926486A (en) * | 2022-05-12 | 2022-08-19 | 哈尔滨工业大学人工智能研究院有限公司 | Thyroid ultrasound image intelligent segmentation method based on multi-level improvement |
| CN114926486B (en) * | 2022-05-12 | 2023-02-07 | 哈尔滨工业大学人工智能研究院有限公司 | Thyroid ultrasound image intelligent segmentation method based on multi-level improvement |
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