A method, device, equipment and medium for predicting dry mouth

Abstract

The present application relates to the field of risk prediction technology, and in particular to a dry mouth prediction method, apparatus, device, and medium, comprising: obtaining three-dimensional dose distribution characteristics and medical imaging characteristics of a target object; weightedly fusing the three-dimensional dose distribution characteristics and the medical imaging characteristics according to a dose weight coefficient and an image weight coefficient to obtain a first mixed feature; wherein the dose weight coefficient and the image weight coefficient are obtained by adjusting the respective weight coefficients of the three-dimensional dose distribution characteristics and medical imaging characteristics of a reference object based on an adaptive average pooling method; and performing dry mouth prediction based on the predicted data to obtain a dry mouth prediction value. By introducing dose distribution information in three-dimensional space, fusing and analyzing the three-dimensional dose distribution characteristics and medical imaging characteristics, and adjusting the weight coefficient based on an adaptive three-dimensional average pooling method, the accuracy of dry mouth prediction is improved and the interpretability of the dry mouth prediction results is improved.

Description

Technical Field

[0001] The present application relates to the technical field of risk prediction, and in particular to a method, device, equipment and medium for predicting dry mouth. Background Art

[0002] NPC is typically caused by abnormal proliferation and malignant changes in nasopharyngeal epithelial cells. Radiotherapy is one of the main treatments for NPC (nasopharyngeal carcinoma), but it can damage the surrounding salivary glands, leading to reduced saliva secretion or changes in saliva quality, which can cause dry mouth. Related art methods for predicting dry mouth use radiomics features combined with clinical characteristics and dose parameters for modeling and prediction. The dose parameters are represented as a one-dimensional discrete form of the dose-volume relationship. The extracted dose parameter data is discrete points, resulting in low prediction accuracy and lack of precision. Summary of the Invention

[0003] The present application provides a dry mouth prediction method, apparatus, device and medium, which solve the technical problem of inaccurate dry mouth prediction in the related art.

[0004] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0005] In a first aspect, embodiments of the present application provide a method for predicting dry mouth, comprising:

[0006] Acquire three-dimensional dose distribution characteristics and medical image characteristics of the target object; wherein the three-dimensional dose distribution characteristics are obtained by feature extraction based on the three-dimensional dose distribution data of the target object;

[0007] The three-dimensional dose distribution feature and the medical image feature are weightedly fused according to a dose weight coefficient and an image weight coefficient to obtain a first hybrid feature; wherein the dose weight coefficient and the image weight coefficient are obtained by adjusting the respective weight coefficients of the three-dimensional dose distribution feature and the medical image feature of the reference object based on an adaptive average pooling method;

[0008] Residual convolution processing is performed based on the first mixed feature to obtain prediction data; and dry mouth prediction is performed based on the prediction data to obtain a dry mouth prediction value.

[0009] This application introduces three-dimensional dose distribution information, fuses and analyzes the three-dimensional dose distribution characteristics and medical imaging characteristics, and adjusts the weight coefficients of the three-dimensional dose distribution characteristics and medical imaging characteristics based on the adaptive three-dimensional average pooling method, thereby improving the accuracy of dry mouth prediction and the interpretability of dry mouth prediction results.

[0010] Optionally, performing residual convolution processing based on the first mixed feature to obtain prediction data includes:

[0011] Performing residual convolution processing based on the first mixed features to obtain processed feature data;

[0012] The processed feature data is fused with the clinical feature data of the target subject to obtain prediction data.

[0013] This application introduces three-dimensional dose distribution information, fuses and analyzes three-dimensional dose distribution characteristics, medical imaging characteristics and clinical characteristic data, and adjusts the weight coefficients of three-dimensional dose distribution characteristics and medical imaging characteristics based on the adaptive three-dimensional average pooling method, thereby improving the accuracy of dry mouth prediction and the interpretability of dry mouth prediction results.

[0014] Optionally, the three-dimensional dose distribution data of the target object is divided into a plurality of segments of three-dimensional dose segmented data; and the step of obtaining the three-dimensional dose distribution characteristics of the target object includes:

[0015] Perform feature extraction on the three-dimensional dose segmentation data to obtain three-dimensional dose segmentation features;

[0016] The three-dimensional dose segmentation features are fused to obtain the three-dimensional dose distribution features.

[0017] This application introduces dose distribution information in three-dimensional space, extracts three-dimensional dose distribution features, medical imaging features and clinical feature data based on deep learning technology and performs fusion analysis, adjusts the weight coefficients of three-dimensional dose distribution features and medical imaging features based on the adaptive three-dimensional average pooling method, and adjusts the weights of each segment of each segmented dose distribution based on the adaptive three-dimensional average pooling method, thereby improving the accuracy of dry mouth prediction and the interpretability of dry mouth prediction results. The higher the weight, the greater the impact on the prediction result.

[0018] Optionally, different segments of the three-dimensional dose segmentation features correspond to different three-dimensional dose segmentation weight coefficients; fusing the three-dimensional dose segmentation features to obtain the three-dimensional dose distribution features includes:

[0019] The three-dimensional dose segment features are weighted and fused according to the three-dimensional dose segment weight coefficient to obtain the three-dimensional dose distribution features.

[0020] Optionally, the three-dimensional dose segmentation weight coefficient corresponding to the three-dimensional dose segmentation feature is obtained by adjusting the weight coefficient of the three-dimensional dose segmentation feature of the reference object based on an adaptive average pooling method.

[0021] Optionally, the three-dimensional dose segmentation data is obtained by dividing the three-dimensional dose distribution data of the target object according to a preset number of segments N; wherein N is an integer greater than or equal to 2.

[0022] The overall dose distribution is divided into five dose segments, namely L, ML, M, MH and H. Taking M as the base and extending to both sides, the dose distribution is divided into uniform intervals by equal division, or by random or normal division. The division method is not unique.

[0023] Optionally, acquiring three-dimensional dose distribution characteristics and medical imaging characteristics of the target object includes:

[0024] Acquire a feature matrix of the parotid gland region of the target object;

[0025] performing convolution processing on the three-dimensional dose distribution data of the target object to obtain an intermediate dose feature, and performing convolution processing on the medical image data of the target object to obtain an intermediate image feature;

[0026] Performing a dot product operation on the feature matrix and the intermediate dose feature to obtain the three-dimensional dose distribution feature;

[0027] Performing a dot product operation on the feature matrix and the intermediate image feature to obtain the medical image feature.

[0028] In a second aspect, an embodiment of the present application provides a dry mouth prediction device, comprising an acquisition module, a weight adjustment module, and a prediction module;

[0029] The acquisition module is used to acquire the three-dimensional dose distribution characteristics and medical image characteristics of the target object; wherein the three-dimensional dose distribution characteristics are obtained by feature extraction based on the three-dimensional dose distribution data of the target object;

[0030] The weight adjustment module is used to perform weighted fusion of the three-dimensional dose distribution feature and the medical image feature according to the dose weight coefficient and the image weight coefficient to obtain a first mixed feature; wherein the dose weight coefficient and the image weight coefficient are obtained by adjusting the respective weight coefficients of the three-dimensional dose distribution feature and the medical image feature of the reference object based on the adaptive average pooling method;

[0031] The prediction module is configured to perform residual convolution processing based on the first mixed feature to obtain prediction data; and perform dry mouth prediction based on the prediction data to obtain a dry mouth prediction value.

[0032] In a third aspect, an embodiment of the present application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the above-mentioned dry mouth prediction method by executing the computer instructions.

[0033] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are used to enable a computer to execute the above-mentioned dry mouth prediction method. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0035] Figure 1 A flowchart of a dry mouth prediction method provided in an embodiment of the present application;

[0036] Figure 2 A structural diagram of a TD model provided in an embodiment of the present application;

[0037] Figure 3 A diagram showing the steps of a dry mouth prediction method provided in an embodiment of the present application;

[0038] Figure 4 A structural diagram of a TDC model provided in an embodiment of the present application;

[0039] Figure 5 A structural diagram of an SD model provided in an embodiment of the present application;

[0040] Figure 6 A structural diagram of an SDC model provided in an embodiment of the present application;

[0041] Figure 7 A comparison chart of the effects of various models provided in the embodiments of this application;

[0042] Figure 8 This is a block diagram of a dry mouth prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] To make the purpose, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0044] NPC (Nasopharyngeal Carcinoma) is a type of cancer that develops in the nasopharynx, also known as nasopharyngeal carcinoma. It is a relatively rare but serious type of cancer that originates in the nasopharynx (nasopharynx), the area connecting the nasal cavity and the throat. NPC typically results from abnormal proliferation and malignant changes in nasopharyngeal epithelial cells. Its specific cause is not fully understood, but it is associated with genetic factors, viral infections (such as Epstein-Barr virus), and environmental factors. Treatments typically include radiotherapy, chemotherapy, and surgery.

[0045] Radiotherapy is one of the main treatments for NPC (nasopharyngeal carcinoma), but it can damage the surrounding salivary glands, leading to reduced saliva secretion or changes in saliva quality, which can cause dry mouth. Dry mouth not only affects patients' oral comfort but can also cause difficulty chewing, swallowing, and speaking, and even increase the risk of oral infection.

[0046] Each patient's tolerance and response to treatment vary. Some patients may be more sensitive to radiation therapy and more prone to dry mouth. The medical team typically takes measures to alleviate dry mouth symptoms, such as oral moisturizers, increased water intake, dietary adjustments, and, if necessary, medication interventions. The medical team also assesses the risk of dry mouth early in the diagnosis and treatment planning stages, and regularly monitors and adjusts treatment plans during treatment to provide optimal treatment outcomes and quality of life.

[0047] The methods for predicting dry mouth in related technologies can be summarized as follows: First, modeling and prediction using radiomics features combined with clinical characteristics. The accuracy of dry mouth prediction results is low. Second, modeling and prediction using radiomics features combined with clinical characteristics and dose parameters. The dose parameters are extracted from the dose-volume histogram (DVH). The DVH represents the distribution information in a one-dimensional discrete form of the dose-volume relationship. The extracted dose parameter data (such as Dmax) are discrete points, with low prediction accuracy and poor interpretation of the prediction results. Third, modeling and prediction using radiomics features combined with clinical characteristics and dosimetry. The dose data covered by dosimetry is larger in magnitude. Furthermore, dosimetry is defined as the application of radiomics methods or deep learning strategies to the three-dimensional dose distribution in radiotherapy to extract dose information relevant to the research objectives.

[0048] The third method is a common one in related technologies and is generally divided into two categories: radiomics based on manually extracted features or dosimetry based on manually defined features, and radiomics based on deep learning. The most commonly used method in related technologies is radiomics based on manually extracted features, which requires additional feature screening for dry mouth prediction, which is time-consuming and labor-intensive.

[0049] The present invention provides a method for predicting dry mouth, which extracts dose features from three-dimensional dose distribution data through deep learning technology, and uses adaptive average pooling technology to adjust the weights of the extracted features to achieve dimensionality reduction and improve the accuracy and interpretability of dry mouth prediction. Figure 1 , Figure 1 A flowchart of a dry mouth prediction method provided in an embodiment of the present application includes:

[0050] S100, obtaining three-dimensional dose distribution characteristics and medical image characteristics of the target object; wherein the three-dimensional dose distribution characteristics are obtained by extracting the three-dimensional dose distribution data of the target object.

[0051] 3D dose distribution data is used to represent dose distribution information in three-dimensional space, while medical imaging data includes CT images and ROI images. Medical imaging features are extracted from the target object's medical imaging data. High-throughput medical imaging features are extracted from regions of interest (ROIs) in radiological images (CT, MR, and PET, etc.) for automated analysis. Key information is extracted from these features to accurately quantify lesions and ultimately used for auxiliary diagnosis, classification, or grading of diseases.

[0052] S200. According to the dose weight coefficient, denoted as W1, and the image weight coefficient, denoted as W2, the three-dimensional dose distribution feature W1 and the medical image feature W2 are weightedly fused to obtain a first mixed feature; wherein, the dose weight coefficient and the image weight coefficient are obtained by adjusting the respective weight coefficients of the three-dimensional dose distribution feature and the medical image feature of the reference object based on the adaptive average pooling method.

[0053] S300 , performing residual convolution processing based on the first mixed feature to obtain prediction data; performing dry mouth prediction based on the prediction data to obtain a dry mouth prediction value.

[0054] Specifically, the reference object is the sample object used in the training of the dry mouth prediction model. Figure 2 , Figure 2 This is a structural diagram of a TD model provided in an embodiment of the present application, Total Dose Distribution. The TD model is a total dose distribution model, which is predicted based on a dry mouth prediction method provided in an embodiment of the present application. The patient's head and neck three-dimensional dose distribution data, head and neck CT, and parotid gland ROI are input into the dry mouth prediction model, and the output is the dry mouth prediction value, that is, the probability of the patient experiencing dry mouth.

[0055] The network architecture of the TD model consists of a sequentially connected convolutional block, three residual convolutional blocks, four dropout layers, an adaptive 3D average pooling layer, and five fully connected layers. The convolutional block contains a 3×3×3 convolutional layer with a stride of 1×1×1, a batch normalization (BN) layer, and a ReLU nonlinear activation layer. Each residual convolutional block includes a 1×1×1 convolutional layer with a stride of 1×1×1, two 3×3×3 convolutional layers with a stride of 1×1×1, two BN layers, and two ReLU layers.

[0056] The coefficients of the four Dropout layers are 0.1, 0.1, 0.08, and 0.05, respectively. After the input image passes through the last Dropout layer, it is converted into a set of feature matrices with a shape of (512, 66, 94, 52). The feature matrix is ​​then input into an adaptive three-dimensional average pooling layer with a kernel size of 4×8×4. After downsampling, it is flattened into a one-dimensional vector M of size 65536, indicating that the TD model extracts information from the input data.

[0057] Reference Figure 3 , Figure 3 A step diagram of a dry mouth prediction method provided in an embodiment of the present application includes the steps of: data collection, data preprocessing, data partitioning, model training and model evaluation.

[0058] S110. Obtain reference data of 243 reference NPC patients, collect three-dimensional dose distribution data, parotid ROI data, and CT data, and perform data preprocessing. The preprocessing includes three-dimensional dose distribution reconstruction and head and neck region interception (dose, CT, ROI). Then, perform three-dimensional convolution operations on the preprocessed three-dimensional dose distribution data, parotid ROI data, and CT data respectively to obtain three-dimensional dose distribution features, denoted as DF, parotid ROI feature matrix, and CT features, denoted as RF.

[0059] S210. Reference data from 243 NPC patients was obtained. Before training the dry mouth prediction model, the obtained reference data set was divided into a training set of 190, a validation set of 23, and a test set of 30. This application trained the dry mouth prediction model on an NVIDIA RTX 3090 based on the Pytorch deep learning framework, using a five-fold cross-validation method for the experiment. Furthermore, to increase data complexity and reduce overfitting of the dry mouth prediction model, data augmentation was performed on the dataset. Specifically, the original sample objects in the training set, validation set, and test set were horizontally flipped and rotated by 10°, 15°, and 20°, respectively.

[0060] S220: Training the initial dry mouth prediction model to obtain a trained dry mouth prediction model, including:

[0061] A loss function of the initial dry mouth prediction model is constructed, and the initial dry mouth prediction model is trained until the loss function converges to a preset value to obtain a trained dry mouth prediction model.

[0062] Specifically, when training the dry mouth prediction model, the binary cross entropy function BCELoss is used as the loss function, and the formula is as follows:

[0063]

[0064] S230. Perform dot multiplication operations on the three-dimensional dose distribution feature DF and the CT feature RF with the parotid gland ROI feature matrix respectively to obtain the three-dimensional dose distribution feature DF' and the CT feature RF' of the parotid gland; adjust the weights of the three-dimensional dose distribution feature DF' and the CT feature RF' of the parotid gland based on the adaptive three-dimensional average pooling method to obtain the weight-adjusted dose weight coefficient and image weight coefficient; splice the three-dimensional dose distribution feature DF' and the CT feature RF' of the parotid gland based on the dose weight coefficient and the image weight coefficient to obtain the first mixed feature, recorded as HF.

[0065] S240, the first mixed feature HF passes through three residual convolution blocks in sequence, namely RS Block1, RS Block2 and RS Block3, and then passes through an adaptive three-dimensional average pooling layer Pooling to obtain a vector M of size 65536, and then passes through the fully connected layer (Fully Connected Layer, FC) to obtain predicted data, recorded as Q, and then after passing through the Sigmoid function, dry mouth prediction is performed based on the predicted data to obtain a dry mouth prediction value. The dry mouth prediction value is a number in the range of [0,1]. The larger the value, the greater the probability of dry mouth in the reference object.

[0066] S250: Training the initial dry mouth prediction model to obtain a trained dry mouth prediction model, including: setting evaluation indicators for the initial dry mouth prediction model until the evaluation indicators are optimized to a preset threshold value to obtain a trained dry mouth prediction model, wherein the evaluation indicators include an area under the curve (AUC), an accuracy (ACC), a precision (Precision), and an F1-score.

[0067] Specifically, the calculation method of the above indicators is as follows:

[0068]

[0069] In the above formula, TP represents the number of samples correctly classified as positive, FP represents the number of samples incorrectly classified as positive, TN represents the number of samples correctly classified as negative, and FN represents the number of samples incorrectly classified as negative.

[0070] S260: Using the trained dry mouth prediction model, an adjusted dose weight coefficient and an image weight coefficient are obtained. Based on the adjusted dose weight coefficient and image weight coefficient, a weighted fusion is performed on the three-dimensional dose distribution characteristics and medical imaging characteristics of the target object to obtain a first hybrid feature of the target object. The dose weight coefficient and the image weight coefficient are obtained by adjusting the respective weight coefficients of the three-dimensional dose distribution characteristics and medical imaging characteristics of the reference object using an adaptive average pooling method.

[0071] S300 , performing residual convolution processing based on the first mixed feature of the target object to obtain prediction data; performing dry mouth prediction based on the prediction data to obtain a dry mouth prediction value.

[0072] This application introduces three-dimensional dose distribution information, fuses and analyzes the three-dimensional dose distribution characteristics and medical imaging characteristics, and adjusts the weight coefficients of the three-dimensional dose distribution characteristics and medical imaging characteristics based on the adaptive three-dimensional average pooling method, thereby improving the accuracy of dry mouth prediction and the interpretability of dry mouth prediction results.

[0073] As an embodiment of the present invention, performing residual convolution processing based on the first mixed feature to obtain prediction data includes:

[0074] Performing residual convolution processing based on the first mixed feature to obtain processed feature data;

[0075] The processed feature data are fused with the clinical feature data of the target subject to obtain the predicted data.

[0076] Among them, the clinical characteristic data of the target object include gender data, age data, T stage data and N stage data, refer to Figure 4 , Figure 4 This is a structural diagram of a TDC model provided in an embodiment of the present application. Based on the TD model, the present application constructs a TDC model (Total Dose Distribution and Clinical, TDC). The network architecture of the TDC model includes a convolution block, three residual convolution blocks, four Dropout layers, an adaptive three-dimensional average pooling layer and three fully connected layers connected in sequence.

[0077] Specifically, the workflow of the TDC model is similar to that of the TD model. Feature extraction is performed on the three-dimensional dose distribution data of the head and neck and the head and neck CT data respectively, and then converted into a one-dimensional vector H of size 256 through the residual convolution block and the fully connected layer.

[0078] The input clinical features are first passed through two fully connected layers to obtain a one-dimensional vector J of size 64, which is then concatenated with H to obtain a one-dimensional vector Q of size 320. Vector Q passes through subsequent fully connected layers to obtain the predicted value of the probability of dry mouth.

[0079] This application introduces three-dimensional dose distribution information, fuses and analyzes three-dimensional dose distribution characteristics, medical imaging characteristics and clinical characteristic data, and adjusts the weight coefficients of three-dimensional dose distribution characteristics and medical imaging characteristics based on the adaptive three-dimensional average pooling method, thereby improving the accuracy of dry mouth prediction and the interpretability of dry mouth prediction results.

[0080] As an embodiment of the present invention, three-dimensional dose distribution data of a target object is divided into a plurality of segments of three-dimensional dose segmented data; obtaining three-dimensional dose distribution characteristics of the target object includes:

[0081] Perform feature extraction on the three-dimensional dose segmentation data to obtain three-dimensional dose segmentation features;

[0082] The three-dimensional dose segmentation features are fused to obtain a three-dimensional dose distribution feature, and N three-dimensional dose segmentation features are superimposed to obtain a fused three-dimensional dose distribution feature.

[0083] Reference Figure 5 , Figure 5 This is a structural diagram of an SD model provided in an embodiment of the present application. Based on the TD model, the present application constructs an SD model (segmented dose distribution). Through the dose segmentation strategy, the overall three-dimensional dose distribution is divided into several three-dimensional dose segments, namely L (0-16Gy), with corresponding weights recorded as W11, ML (16-32Gy), with corresponding weights recorded as W12, M (32-48Gy), with corresponding weights recorded as W13, MH (48-64Gy), with corresponding weights recorded as W14 and H (above 64Gy), with corresponding weights recorded as W15. The weights of the five segments are recorded as W11, W12, W13, W14 and W15 respectively.

[0084] Convolution extraction was performed on the five segmented dose distributions respectively to obtain the corresponding dose omics features based on the segmented dose distribution. Subsequently, each dose omics feature was multiplied by the corresponding weight and then superimposed.

[0085] Reference Figure 6 , Figure 6This is a structural diagram of an SDC model provided in an embodiment of the present application, Segmented DoseDistribution and Clinical. The input of the SDC model is three-dimensional dose distribution data of the head and neck, CT data, parotid gland ROI data and clinical feature data, and clinical features are added on the basis of the SD model. The SDC model first segments the overall three-dimensional dose distribution, performs separate feature extraction on each segmented dose distribution, and obtains 5 segmented dose features. The segmented dose features are then fused and spliced ​​with the image features to obtain a mixed feature HF. After the subsequent residual convolution block and the fully connected layer, HF obtains 256 features. The above 256 features are then spliced ​​with the clinical feature data that have passed through two fully connected layers to obtain the matrix Q. After the matrix Q passes through a fully connected layer, the predicted value of dry mouth is obtained.

[0086] It is understandable that the embodiment of the present application provides an initial model, and only clinical feature data is input into the initial model, which has a poor prediction effect.

[0087] Furthermore, after each model is trained, each weight coefficient is solidified separately, which is convenient for subsequent direct use, saving time and effort. Depending on whether clinical characteristic data is input and whether dose segmentation operation is performed, the embodiment of the present application respectively establishes TD model (overall dose distribution model), TDC model (overall dose distribution + clinical characteristic model), SD model (segmented dose distribution model) and SDC model (segmented dose distribution + clinical characteristic model), referring to Figure 7 , Figure 7 This is a comparison chart of the effects of various models provided in the examples of this application. The differences between the dry mouth prediction models provided in the examples of this application are shown in Table 1 below:

[0088] Table 1 Input feature information of different dry mouth prediction models

[0089]

[0090] In the table, “√” indicates that the current model uses the selected features as input. Figure 7 It can be seen that the SDC model has the best prediction effect.

[0091] This application introduces dose distribution information in three-dimensional space, extracts three-dimensional dose distribution features, medical imaging features and clinical feature data based on deep learning technology and performs fusion analysis, adjusts the weight coefficients of three-dimensional dose distribution features and medical imaging features based on the adaptive three-dimensional average pooling method, and adjusts the weights of each segment of each segmented dose distribution based on the adaptive three-dimensional average pooling method, establishes multiple end-to-end dry mouth prediction models, improves the accuracy of dry mouth prediction, and improves the interpretability of dry mouth prediction results. The higher the weight, the greater the impact on the prediction results.

[0092] As an embodiment of the present invention, different segments of the three-dimensional dose segmentation features correspond to different three-dimensional dose segmentation weight coefficients; the three-dimensional dose segmentation features are fused to obtain a three-dimensional dose distribution feature, including:

[0093] The three-dimensional dose segment features are weighted and fused according to the three-dimensional dose segment weight coefficient to obtain the three-dimensional dose distribution features.

[0094] As an embodiment of the present invention, the 3D dose segmentation weight coefficient corresponding to the 3D dose segmentation feature is obtained by adjusting the weight coefficient of the 3D dose segmentation feature of the reference object based on the adaptive average pooling method.

[0095] As an embodiment of the present invention, the three-dimensional dose segmentation data is obtained by dividing the three-dimensional dose distribution data of the target object according to a preset number of segments N; wherein N is an integer greater than or equal to 2.

[0096] Specifically, the overall dose distribution is divided into five dose segments, namely L, ML, M, MH and H. Taking M as the base and extending to both sides, the dose distribution is divided into uniform intervals by equal division, or by random or normal division. The division method is not unique.

[0097] As an embodiment of the present invention, obtaining three-dimensional dose distribution characteristics and medical image characteristics of a target object includes:

[0098] Obtain the feature matrix of the parotid gland region of the target object;

[0099] Performing convolution processing on the three-dimensional dose distribution data of the target object to obtain an intermediate dose feature, and performing convolution processing on the medical image data of the target object to obtain an intermediate image feature;

[0100] Perform dot multiplication on the feature matrix and the intermediate dose feature to obtain the three-dimensional dose distribution feature;

[0101] Perform dot product operation on the feature matrix and the intermediate image features to obtain the medical image features.

[0102] Reference Figure 8 , Figure 8 This is a block diagram of a dry mouth prediction device provided in an embodiment of the present application. The present application provides a dry mouth prediction device, including an acquisition module 1010, a weight adjustment module 1020 and a prediction module 1030.

[0103] The acquisition module 1010 is used to acquire three-dimensional dose distribution characteristics and medical image characteristics of the target object; wherein the three-dimensional dose distribution characteristics are obtained by feature extraction based on the three-dimensional dose distribution data of the target object.

[0104] The weight adjustment module 1020 is used to perform weighted fusion on the three-dimensional dose distribution characteristics and the medical imaging characteristics according to the dose weight coefficient and the image weight coefficient to obtain a first mixed feature; wherein, the dose weight coefficient and the image weight coefficient are obtained by adjusting the respective weight coefficients of the three-dimensional dose distribution characteristics and the medical imaging characteristics of the reference object based on the adaptive average pooling method.

[0105] The prediction module 1030 is configured to perform residual convolution processing based on the first mixed feature to obtain prediction data; and perform dry mouth prediction based on the prediction data to obtain a dry mouth prediction value.

[0106] As an embodiment of the present invention, the prediction module 1030 includes:

[0107] Performing residual convolution processing based on the first mixed features to obtain processed feature data;

[0108] The processed feature data is fused with the clinical feature data of the target subject to obtain prediction data.

[0109] As an embodiment of the present invention, the three-dimensional dose distribution data of the target object is divided into a plurality of segments of three-dimensional dose segmented data; the acquisition module 1010 includes:

[0110] Perform feature extraction on the three-dimensional dose segmentation data to obtain three-dimensional dose segmentation features;

[0111] The three-dimensional dose segmentation features are fused to obtain the three-dimensional dose distribution features.

[0112] As an embodiment of the present invention, different three-dimensional dose segmentation features correspond to different three-dimensional dose segmentation weight coefficients; the acquisition module 1010 further includes:

[0113] The three-dimensional dose segment features are weighted and fused according to the three-dimensional dose segment weight coefficient to obtain the three-dimensional dose distribution features.

[0114] As an embodiment of the present invention, the three-dimensional dose segmentation weight coefficient corresponding to the three-dimensional dose segmentation feature is obtained by adjusting the weight coefficient of the three-dimensional dose segmentation feature of the reference object based on the adaptive average pooling method.

[0115] As an embodiment of the present invention, the three-dimensional dose segmentation data is obtained by dividing the three-dimensional dose distribution data of the target object according to a preset number of segments N; wherein N is an integer greater than or equal to 2.

[0116] As an embodiment of the present invention, the acquisition module 1010 includes:

[0117] Acquire a feature matrix of the parotid gland region of the target object;

[0118] performing convolution processing on the three-dimensional dose distribution data of the target object to obtain an intermediate dose feature, and performing convolution processing on the medical image data of the target object to obtain an intermediate image feature;

[0119] Performing a dot product operation on the feature matrix and the intermediate dose feature to obtain the three-dimensional dose distribution feature;

[0120] Performing a dot product operation on the feature matrix and the intermediate image feature to obtain the medical image feature.

[0121] The present application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform a dry mouth prediction method provided in the above embodiment.

[0122] This computer equipment comprises: one or more processors, memory, and the interface for connecting each component, including high-speed interface and low-speed interface. Each component utilizes different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer equipment, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer equipment can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system).

[0123] The processor may be a central processing unit, a network processor, or a combination thereof. The processor may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0124] The memory stores instructions that can be executed by at least one processor, so that the at least one processor executes the method shown in the above embodiment.

[0125] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the storage may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0126] The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid state drive; the memory may also include a combination of the above types of memory.

[0127] The computer device further includes a communication interface for the computer device to communicate with other devices or a communication network.

[0128] The present application provides a computer-readable storage medium having computer instructions stored thereon. The computer instructions are used to enable a computer to execute a dry mouth prediction method provided in the above embodiment.

[0129] The embodiments of the present application also provide a computer-readable storage medium. The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0130] An embodiment of the present application provides a computer program product, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a method according to any embodiment of the present application.

[0131] Although the embodiments of the present application have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations shall fall within the scope defined by the appended claims.

[0132] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0133] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0134] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0135] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0138] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0139] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0140] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

[0141] Although the embodiments of the present application have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A method for predicting dry mouth, characterized in that: include: Acquire three-dimensional dose distribution characteristics and medical imaging characteristics of a target object; wherein the target object is a nasopharyngeal carcinoma radiotherapy patient, the medical imaging characteristics are obtained by extracting characteristics based on the medical imaging data of the target object, the three-dimensional dose distribution data is three-dimensional dose distribution data of the head and neck, and the medical imaging data is a head and neck CT image and a parotid gland ROI image; the three-dimensional dose distribution characteristics are obtained by extracting characteristics based on the three-dimensional dose distribution data of the target object; the three-dimensional dose distribution data of the target object is divided into three-dimensional dose segmentation data of several segments; the acquisition of the three-dimensional dose distribution characteristics of the target object includes: extracting characteristics from the three-dimensional dose segmentation data to obtain three-dimensional dose segmentation characteristics; three-dimensional dose segmentation characteristics of different segments correspond to different three-dimensional dose segmentation weight coefficients; the three-dimensional dose segmentation characteristics are weightedly fused according to the three-dimensional dose segmentation weight coefficient to obtain the three-dimensional dose distribution characteristics; the three-dimensional dose segmentation weight coefficient corresponding to the three-dimensional dose segmentation characteristic is obtained by adjusting the weight coefficient of the three-dimensional dose segmentation characteristic of the reference object based on the adaptive average pooling method; The three-dimensional dose distribution feature and the medical image feature are weightedly fused according to a dose weight coefficient and an image weight coefficient to obtain a first hybrid feature; wherein the dose weight coefficient and the image weight coefficient are obtained by adjusting the respective weight coefficients of the three-dimensional dose distribution feature and the medical image feature of the reference object based on an adaptive average pooling method; Residual convolution processing is performed based on the first mixed feature to obtain processed feature data; the processed feature data is fused with the clinical feature data of the target object to obtain predicted data; and dry mouth prediction is performed based on the predicted data to obtain a dry mouth prediction value.

2. The method according to claim 1, characterized in that The three-dimensional dose segmentation data is obtained by dividing the three-dimensional dose distribution data of the target object according to a preset number N of segments.

3. The method according to claim 2, characterized in that N is an integer greater than or equal to 2.

4. A dry mouth prediction device, characterized in that: Includes acquisition module, weight adjustment module and prediction module; The acquisition module is used to acquire the three-dimensional dose distribution characteristics and medical imaging characteristics of the target object; wherein the target object is a nasopharyngeal carcinoma radiotherapy patient, the medical imaging characteristics are obtained by feature extraction based on the medical imaging data of the target object, the three-dimensional dose distribution data is three-dimensional dose distribution data of the head and neck, and the medical imaging data is a head and neck CT image and a parotid gland ROI image; the three-dimensional dose distribution characteristics are obtained by feature extraction based on the three-dimensional dose distribution data of the target object; the three-dimensional dose distribution data of the target object is divided into three-dimensional dose segmentation data of several segments; the acquisition of the three-dimensional dose distribution characteristics of the target object includes: extracting features from the three-dimensional dose segmentation data to obtain three-dimensional dose segmentation characteristics; three-dimensional dose segmentation characteristics of different segments correspond to different three-dimensional dose segmentation weight coefficients; the three-dimensional dose segmentation characteristics are weightedly fused according to the three-dimensional dose segmentation weight coefficient to obtain the three-dimensional dose distribution characteristics; the three-dimensional dose segmentation weight coefficient corresponding to the three-dimensional dose segmentation characteristic is obtained by adjusting the weight coefficient of the three-dimensional dose segmentation characteristic of the reference object based on the adaptive average pooling method; The weight adjustment module is used to perform weighted fusion of the three-dimensional dose distribution feature and the medical image feature according to the dose weight coefficient and the image weight coefficient to obtain a first mixed feature; wherein the dose weight coefficient and the image weight coefficient are obtained by adjusting the respective weight coefficients of the three-dimensional dose distribution feature and the medical image feature of the reference object based on the adaptive average pooling method; The prediction module is configured to perform residual convolution processing based on the first mixed feature to obtain processed feature data; fuse the processed feature data with the clinical feature data of the target subject to obtain predicted data; and perform dry mouth prediction based on the predicted data to obtain a dry mouth prediction value.

5. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the dry mouth prediction method according to any one of claims 1 to 3 by executing the computer instructions.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the dry mouth prediction method according to any one of claims 1 to 3.

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