Patient condition tracking method and system

Abstract

The invention discloses a patient condition tracking method and system, and relates to the technical field of medical information processing, and the method comprises the steps: collecting the focus image data of a patient, and carrying out the rigid registration of the focus image data through affine transformation; focus probability distribution is predicted based on a deep convolutional neural network, and focus segmentation and marking are carried out based on a conditional random field CRF. According to the method, through the combination of the deep convolutional neural network and the conditional random field, the accuracy of focus region segmentation is improved, human intervention is reduced, through the combination of rigid registration and model recognition, global alignment of images can be quickly realized, the change of the focus region can be tracked in a detailed manner, and the accuracy of focus region segmentation is improved. The displacement of pixels can be smoothly interpolated between control points through non-rigid registration of the B-spline primary function, the generated image can be accurately aligned with the form and position of a focus area, and reliable basic data is provided for subsequent work such as focus change analysis and form change quantification.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information processing, and in particular to a method and system for tracking a patient's condition. Background Art

[0002] With the rapid development of medical imaging technology, the acquisition and analysis of medical images play a vital role in clinical diagnosis, treatment plan formulation, and efficacy evaluation. Traditional medical imaging equipment, such as CT, MRI, and PET-CT, can provide detailed anatomical and functional information, allowing doctors to observe the morphology, location, and size of lesions. Existing lesion tracking methods mostly rely entirely on manual image analysis, which is not only time-consuming and labor-intensive, but also susceptible to human factors. In order to improve the degree of automation of image analysis, a variety of automated image analysis methods based on machine learning and deep learning have emerged in recent years. In particular, lesion recognition and segmentation technologies based on algorithms such as convolutional neural networks (CNNs) have made significant progress. However, existing technologies still have shortcomings in several key aspects. First, traditional image registration methods usually rely on global rigid transformations. Although they work well in global image alignment, they cannot cope with local morphological changes in the lesion area, especially in tracking special lesions and lesions that change greatly over time. They show great limitations. Second, although deep convolutional neural networks have been widely used in lesion segmentation, relying solely on CNN for segmentation may still face problems such as blurred boundaries and high misclassification rates. In particular, under different imaging conditions or noise interference, the segmentation accuracy of the lesion area is difficult to guarantee, and it is difficult to accurately reflect the dynamic change trend of the lesion. Especially in long-term tracking, it is difficult to effectively predict the volume growth rate and change trend of the lesion. Summary of the Invention

[0003] In view of the problems existing in the above-mentioned existing patient condition tracking methods and systems, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that traditional image registration methods usually rely on global rigid transformation. Although they are effective in global image alignment, they cannot cope with local morphological changes in the lesion area, especially in tracking lesions that change greatly over time, such as special lesions. They show great limitations. Secondly, although deep convolutional neural networks have been widely used in lesion segmentation, relying solely on CNN for segmentation may still face problems such as blurred boundaries and high misclassification rates. Especially under different imaging conditions or noise interference, the segmentation accuracy of the lesion area is difficult to guarantee, and it is difficult to accurately reflect the dynamic change trend of the lesion. Especially in long-term tracking, it is difficult to effectively predict the volume growth rate and change trend of the lesion.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a patient condition tracking method, comprising: Collect patient lesion image data and perform rigid registration on the lesion image data through affine transformation; Lesion probability distribution is predicted based on deep convolutional neural networks, and lesion segmentation and labeling is performed based on conditional random fields (CRFs); The B-spline basis function is used to describe the local displacement and perform non-rigid registration on the image data of the lesion area to generate the final image; The lesion area was selected for volume calculation, and the lesion volume growth rate and change rate were determined using nonlinear differential equations.

[0006] As a preferred solution of the patient condition tracking method of the present invention, the collecting of patient lesion image data includes: Collect image data based on the patient's specific condition needs; Determine the frequency of image data acquisition based on physician experience; Preprocessing is performed based on image data, including image data denoising, grayscale normalization, and image cropping.

[0007] As a preferred solution of the patient condition tracking method of the present invention, the rigid registration of lesion image data by affine transformation includes: Based on the preprocessed image data, the image data of multiple time points are preliminarily aligned through affine transformation. The affine transformation matrix is ​​defined, and the new position of each pixel is calculated through matrix multiplication and vector addition operations for rigid registration, which is expressed as: in Indicates the changed coordinates of the pixel coordinate point. Represents the pixel coordinates of the original image, and b are the affine transformation matrix and displacement vector respectively; A reference image is determined based on the training set, and the mean square error (MSE) is selected as the similarity metric function. The goal is to minimize the difference between the target image and the reference image. It is iteratively optimized using the Adam optimizer and gradient descent. When the calculated loss no longer decreases significantly during consecutive iterations, the iteration is stopped, the optimal A and b are determined, and the optimal affine transformation matrix is ​​output. Through rigid registration, the coordinates of each pixel are transformed to the new position coordinates , generate aligned images.

[0008] As a preferred embodiment of the patient condition tracking method of the present invention, the method of predicting the probability distribution of lesions based on a deep convolutional neural network and performing lesion segmentation and labeling based on a conditional random field (CRF) includes: A lesion area model is constructed based on a deep convolutional neural network, including an input layer, a convolutional layer, a maximum pooling layer, a fully connected layer, and an output layer; The model is trained using the training set, and the cross-entropy loss function is selected to calculate the difference between the model prediction results and the actual labels of the lesion area. The Adam optimizer is used for gradient descent optimization to update the model parameters. If the model loss no longer decreases significantly during the continuous iteration process, the iteration output model is stopped. The lesion region segmentation prediction result output by the lesion region model is used to obtain a lesion probability distribution map based on the aligned image; Based on the conditional random field (CRF), the probability of each pixel belonging to the lesion area is taken as a single-point term, the similarity between pixels is defined as a double-point term, and the energy function is defined based on the single-point term and the double-point term, which is expressed as: ; ; ;in Indicates the cost of the i-th pixel belonging to the lesion, represents the probability that the i-th pixel belongs to the lesion area, Represents the similarity cost between the adjacent i-th pixel and j-th pixel, and Represent the spatial coordinates of the i-th pixel and the j-th pixel respectively, represents the weight coefficient, and Represent the grayscale values ​​of the i-th pixel and the j-th pixel respectively, and represent the spatial scale parameter and color scale parameter respectively, Represents the total energy function value, N represents the total number of pixels, Represents the neighboring pixel area of ​​the i-th pixel; Use the training set and select the cross entropy loss function to calculate the loss between the lesion prediction results of the conditional random field CRF and the actual label, use the Adam optimizer for gradient descent optimization, and update the conditional random field CRF parameters, including 、 and , if the calculated loss no longer decreases significantly during the continuous iteration process, the iteration is stopped, the model parameters are output, and the model is updated; The mean-field approximation method is used to optimize the energy function and determine the optimization formula, which is expressed as: ;in represents the normalization factor, represents the classification probability, Represents the classification probability of adjacent pixels; The predicted probability map of the lesion area model is used as the initial ,Sure ; based on The probability distribution of each pixel is updated, and the classification probability of each pixel is updated. If the change in the value of is no longer significantly reduced, the iteration is stopped and the final value of the model is output. ; Based on the final Generate the corresponding lesion area probability map, segment the pixel values ​​of the probability map based on the historical average value as the segmentation threshold, and mark the lesion area.

[0009] As a preferred embodiment of the patient condition tracking method of the present invention, the method of using a B-spline basis function to describe the local displacement and performing non-rigid registration on the image data of the lesion area to generate the final image includes: Generate a grid based on the lesion area of ​​the image data, define the grid nodes as control points, and perform non-rigid registration; Based on each control point, the B-spline basis function is used to describe the local displacement, which is expressed as: ; ;in Represents the displacement of pixel point x, n represents the number of control points adjacent to the pixel point, represents the displacement vector of control point i, represents the B-spline basis function, represents the coordinates of the i-th control point, represents the third-order B-spline basis function; The reference image is determined based on the training set, and the mean square error (MSE) is selected as the similarity measurement function. The goal is to minimize the difference between the target image and the reference image, which is expressed as: ;in represents the non-rigid registration calculation error, Indicates the total number of pixels, represents the coordinates of the i-th pixel, is the corresponding pixel coordinate in the reference image, M represents the total number of control points, represents the smoothness regularization parameter, which is determined based on historical data, represents the displacement vector gradient of control point j, Represents pixel points The displacement of Use the Levenberg-Marquardt optimization algorithm to optimize the displacement of the current control point , calculate the value of the non-rigid registration calculation error, calculate the objective function for each control point displacement The gradient of the control point is then updated. When the calculated error no longer decreases significantly during the continuous optimization process, the iteration is stopped and the displacement of the control point is optimized. ; Calculate pixel displacement based on control point displacement through non-rigid registration , and further adjust the pixel positions based on the aligned image to generate the final image.

[0010] As a preferred solution of the patient condition tracking method of the present invention, wherein: the selecting of the lesion area for volume calculation includes: Based on image scanning equipment, three-dimensional image data is formed by stacking multiple layers of image data. Based on the final image generated after non-rigid registration, the lesion change area is determined and the voxels in the lesion change area are selected for volume calculation.

[0011] As a preferred embodiment of the patient condition tracking method of the present invention, the method of determining the lesion volume growth rate and change rate using a nonlinear differential equation includes: The volume change data of the lesion is fitted using a nonlinear differential equation, which is expressed as: ;in represents the lesion volume at time t, represents the growth rate of the lesion volume, Indicates the maximum limit value of the lesion volume; Using historical data and using squared error as the loss function, the difference between the observed volume and the predicted volume is minimized. Use nonlinear least squares method to iteratively calculate the loss. If the loss of the model no longer decreases significantly during the continuous iteration, the iteration is stopped and the final K and ; The rate of change of the growth rate was determined based on the change in the lesion volume growth rate at different times, and the rate of change was evaluated based on the physician's experience.

[0012] Another object of the present invention is to provide a system for tracking a patient's condition, comprising: An image data processing module is used to collect the patient's lesion image data and perform image preprocessing; Rigid registration module, used for rigid registration through affine transformation; Lesion recognition and segmentation module, which is used to predict the probability distribution of lesion areas based on deep convolutional neural networks and use conditional random fields for lesion segmentation and labeling; The non-rigid registration module is used to describe the local displacement through the B-spline basis function and perform non-rigid registration; The lesion volume calculation module is used to select the lesion area for volume calculation based on the image data after non-rigid registration; The volume change analysis module is used to determine the volume growth rate and change rate of the lesion using nonlinear differential equations.

[0013] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned patient condition tracking method when executing the computer program.

[0014] A computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned patient condition tracking method when executed by a processor.

[0015] The beneficial effects of the present invention are: through the combination of deep convolutional neural networks and conditional random fields, the accuracy of lesion area segmentation is improved and human intervention is reduced. Through the combination of rigid registration and model recognition, global alignment of images can be quickly achieved, so that changes in the lesion area can be tracked in detail. Through the non-rigid registration of the B-spline basis function, the displacement of pixels can be smoothly interpolated between control points, and the generated image can accurately align the morphology and position of the lesion area, providing reliable basic data for subsequent lesion change analysis, morphological change quantification and other work. The volume calculation through voxels can provide precise and reliable lesion volume measurement, especially for complex lesion morphology or fuzzy lesion edges. By using historical lesion volume data and fitting the model parameters through nonlinear least squares method, the volume change of the lesion in the future can be accurately predicted, which can help doctors predict the development trend of the lesion. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 Flowchart of the patient tracking method.

[0018] Figure 2 This is a structural diagram of the patient condition tracking system. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1, with reference to Figure 1 , which is the first embodiment of the present invention, provides a method for tracking a patient's condition, the method comprising: S1, collect patient lesion image data and perform rigid registration on the lesion image data through affine transformation; Preferably, collecting patient lesion image data includes: Image data collection is performed based on the patient's specific condition. CT, MRI, PET-CT, or ultrasound can be used for image data collection. CT is used for hard tissue imaging, MRI is used for soft tissue imaging, PET-CT is suitable for metabolic imaging, and ultrasound is suitable for real-time dynamic imaging. Determine the frequency of image data acquisition based on physician experience; Preprocessing is performed based on image data, including image data denoising, grayscale normalization, and image cropping.

[0023] By determining a reasonable frequency of imaging data acquisition based on the patient's specific condition and historical data on disease progression, we can avoid excessive repeated examinations, reduce the patient's burden and radiation exposure, and reduce acquisition costs. The denoising process can significantly improve image clarity and remove random errors caused by noise in imaging equipment (such as artifacts in MRI and radiation noise in CT). The denoised image can more clearly show the boundaries and details of the lesion. Grayscale normalization can standardize the grayscale values ​​of images from different devices or at different time points to a unified range, making the images consistent. Image cropping can remove irrelevant areas in the image and reduce data redundancy.

[0024] Furthermore, the lesion image data is rigidly registered through affine transformation, including: Based on the preprocessed image data, the image data of multiple time points are preliminarily aligned through affine transformation. The affine transformation matrix is ​​defined, and the new position of each pixel is calculated through matrix multiplication and vector addition operations for rigid registration, which is expressed as: in Indicates the changed coordinates of the pixel coordinate point. Represents the pixel coordinates of the original image, and b are the affine transformation matrix and displacement vector, respectively, expressed as: ; ; Where A is a 3*3 affine transformation matrix used to express rotation and scaling, and each matrix element Controls that are clearly defined as rotation, scaling, or shearing operations, using image metadata (such as the image's posture information) or image analysis tools to obtain the image's rotation angle on each axis, and the scaling factor by comparing the image's pixel resolution. 、 and The displacement values ​​along the x, y, and z axes in the displacement vector are calculated by using image analysis tools (such as the automatic feature point detection algorithm SURF) to determine the position difference of the same feature point at different time points, thereby calculating the translation vector; The reference image is determined based on the training set, and the mean square error (MSE) is selected as the similarity measurement function. The goal is to minimize the difference between the target image and the reference image, which is expressed as: ;in represents the calculation error, Indicates the total number of pixels, represents the coordinates of the i-th pixel, is the coordinate of the corresponding pixel in the reference image; Iterate the optimization using the Adam optimizer and gradient descent method. When the calculated loss no longer decreases significantly during the continuous iteration, the iteration is stopped, the optimal A and b are determined, and the optimal affine transformation matrix is ​​output. Through rigid registration, the coordinates of each pixel are transformed to the new position coordinates , generate aligned images.

[0025] Through rigid registration based on affine transformation, slight differences in patient posture during medical imaging can be compensated, so that image data at different time points can be spatially aligned, eliminating errors caused by changes in position and angle. This allows subsequent analysis to focus on changes in the lesion itself without being affected by deviations during image acquisition, providing a reliable data basis for subsequent predictions of lesion volume changes and morphological change trends.

[0026] S2, predicts the probability distribution of lesions based on deep convolutional neural networks, and performs lesion segmentation and labeling based on conditional random fields (CRF); Preferably, the lesion probability distribution is predicted based on a deep convolutional neural network, and the lesion segmentation and labeling is performed based on a conditional random field (CRF), including: A lesion area model is constructed based on a deep convolutional neural network, including an input layer, a convolutional layer, a maximum pooling layer, a fully connected layer, and an output layer; The input layer inputs the aligned image after rigid adaptation. The convolution layer extracts local features by scanning local areas of the image. The maximum pooling layer reduces the size of the feature map and highlights the significance of the features. The fully connected layer maps the features to the output layer, which outputs the predicted results of the lesion area segmentation. The model is trained using the training set, and the cross-entropy loss function is selected to calculate the difference between the model prediction results and the actual labels of the lesion area. The Adam optimizer is used for gradient descent optimization to update the model parameters. If the model loss no longer decreases significantly during the continuous iteration process, the iteration output model is stopped. The lesion region segmentation prediction result output by the lesion region model is used to obtain a lesion probability distribution map based on the aligned image; Based on the conditional random field (CRF), the probability of each pixel belonging to the lesion area is taken as a single-point term, the similarity between pixels is defined as a double-point term, and the energy function is defined based on the single-point term and the double-point term, which is expressed as: ; ; ;in Indicates the cost of the i-th pixel belonging to the lesion, represents the probability that the i-th pixel belongs to the lesion area, Represents the similarity cost between the adjacent i-th pixel and j-th pixel, and Represent the spatial coordinates of the i-th pixel and the j-th pixel respectively, represents the weight coefficient, and Represent the grayscale values ​​of the i-th pixel and the j-th pixel respectively, and represent the spatial scale parameter and color scale parameter respectively, Represents the total energy function value, N represents the total number of pixels, represents the neighboring pixel area of ​​the i-th pixel, represents the Euclidean distance between the i-th pixel and the j-th pixel; Use the training set and select the cross entropy loss function to calculate the loss between the lesion prediction results of the conditional random field CRF and the actual label, use the Adam optimizer for gradient descent optimization, and update the conditional random field CRF parameters, including 、 and , if the calculated loss no longer decreases significantly during the continuous iteration process, the iteration is stopped, the model parameters are output, and the model is updated; The mean-field approximation method is used to optimize the energy function and determine the optimization formula, which is expressed as: ;in represents the normalization factor, represents the classification probability, Represents the classification probability of adjacent pixels; The predicted probability map of the lesion area model is used as the initial ,Sure ; based on The probability distribution of each pixel is updated, and the classification probability of each pixel is updated. If the change in the value of is no longer significantly reduced, the iteration is stopped and the final value of the model is output. ; Based on the final Generate the corresponding lesion area probability map, segment the pixel values ​​of the probability map based on the historical average value as the segmentation threshold, and mark the lesion area.

[0027] By using a deep convolutional neural network, local features can be extracted from scanned images, and important information such as the edges, shape, and texture of the lesion area can be learned. The maximum pooling layer reduces the size of the feature map, reducing the computational burden while retaining the important information of the features. Through the fully connected layer and output layer, the model can map the image features to the probability space and generate the predicted probability distribution of the lesion area, achieving basic recognition based on the scanned image. By using conditional random fields (CRFs), the lesion probability of each pixel and the similarity cost of adjacent pixels are modeled based on the definition of single-point terms and double-point terms. Combined with the optimization of the mean field approximation method, the classification probability of each pixel is gradually updated. This allows each pixel to consider not only its own prediction results but also the influence of adjacent pixels, making the pixel classification more robust and effectively solving the classification error problem caused by local noise in the image. Through multiple iterative updates, the probability distribution map of the lesion area can be gradually optimized, making the segmentation result more refined and accurate. By combining deep convolutional neural networks and conditional random fields (CRF), the accuracy of lesion area segmentation is improved, human intervention is reduced, and it can adapt to complex lesion morphology. Lesions can be marked efficiently and accurately, providing a reliable basis for subsequent diagnosis and treatment planning.

[0028] S3, using B-spline basis functions to describe local displacements for non-rigid registration to generate the final image; Preferably, a B-spline basis function is used to describe the local displacement for non-rigid registration to generate the final image, including: Generate a grid based on the lesion area of ​​the image data, define the grid nodes as control points, and perform non-rigid registration; Based on each control point, the B-spline basis function is used to describe the local displacement, which is expressed as: ; ;in Represents the displacement of pixel point x, n represents the number of control points adjacent to the pixel point, represents the displacement vector of control point i, represents the B-spline basis function, represents the coordinates of the i-th control point, represents the third-order B-spline basis function, which is expressed as: ; Determine the reference image based on the training set, select the mean square error MSE as the similarity measurement function, and the goal is to minimize the difference between the target image and the reference image, which is expressed as: ;in represents the non-rigid registration calculation error, Indicates the total number of pixels, represents the coordinates of the i-th pixel, is the corresponding pixel coordinate in the reference image, M represents the total number of control points, represents the smoothness regularization parameter, which is determined based on historical data, represents the displacement vector gradient of control point j, Represents pixel points The displacement of Use the Levenberg-Marquardt optimization algorithm to optimize the displacement of the current control point , calculate the value of the non-rigid registration calculation error, calculate the objective function for each control point displacement The gradient of the control point is then updated. When the calculated error no longer decreases significantly during the continuous optimization process, the iteration is stopped and the displacement of the control point is optimized. ; Calculate pixel displacement based on control point displacement through non-rigid registration , and further adjust the pixel positions based on the aligned image to generate the final image.

[0029] By generating a grid in the lesion area and defining the grid nodes as control points, a foundation is provided for non-rigid registration to accurately control the local deformation of the lesion area. The B-spline basis function can smoothly interpolate the pixel displacement between the control points, ensuring that the change in local displacement is smooth and does not produce abrupt changes, avoiding artifacts or unnatural edges that may appear in the registration results. The third-order B-spline basis function allows the registration process to capture smaller morphological changes in the lesion area, such as subtle deformation of the lesion edge, allowing non-rigid registration to align the boundaries of the lesion area in local details, providing higher segmentation accuracy. The Levenberg-Marquardt optimization algorithm combines the advantages of gradient descent and Newton's method to quickly and efficiently find the optimal displacement vector of the control point. By calculating the displacement of pixel points through the displacement of control points, each pixel can be accurately moved to its new position, achieving higher-precision alignment of the lesion area. In particular, when the lesion area undergoes significant morphological changes at different time points, non-rigid registration can accurately capture these changes. After global and local adjustments to the image based on the displacement of control points, the generated image can accurately align the morphology and position of the lesion area, providing reliable basic data for subsequent lesion change analysis and morphological change quantification, and can significantly improve the accuracy and consistency of image analysis. The model-identified lesion region probability map provides an accurate initial lesion location for registration, while non-rigid registration further improves recognition accuracy through local adjustments. This collaborative optimization cannot be achieved by a single technology alone. Especially in complex lesion morphological changes, the combined effect can significantly reduce errors. At the same time, the combination of rigid and non-rigid registration greatly improves the stability of registration, avoiding the global instability that may be caused by relying solely on non-rigid registration. By combining rigid registration with model recognition, global alignment of images can be quickly achieved, allowing for detailed tracking of changes in the lesion region. In particular, subtle changes in lesion expansion or contraction during disease progression can be captured by non-rigid registration, providing a reliable initial location for subsequent local lesion changes, ensuring accurate alignment and analysis of lesion regions during subtle morphological changes, assisting doctors in tracking patients' conditions and helping them dynamically track changes in lesion regions through image data. Especially for patients undergoing long-term treatment, doctors can use these image changes to analyze the effectiveness of treatment and make corresponding adjustments. Automated image analysis greatly reduces the workload of doctors in manual labeling and analysis, and the algorithm enables more detailed lesion region analysis, improving the intelligence level of image analysis.

[0030] S4, select the lesion area for volume calculation, and use nonlinear differential equations to determine the lesion volume growth rate and change rate; Preferably, the lesion area is selected for volume calculation, including: Based on the image scanning equipment, three-dimensional image data is formed by stacking multiple layers of image data. Based on the final image generated by non-rigid registration, the lesion change area is determined and the voxels in the lesion change area are selected for volume calculation, which is expressed as: ; Where V represents the volume of the lesion area, 、 and They represent the length, width and height of the voxel in three-dimensional space respectively, i, j and k are the indexes of the voxel respectively, representing the voxels in all lesion areas on the three-dimensional grid.

[0031] Calculating volume through voxels can provide precise and reliable lesion volume measurements, especially for complex lesion morphologies or lesion edges with blurred edges. It can capture the true spatial distribution of the lesion. Through non-rigid registration and voxel volume calculation, its three-dimensional volume can be analyzed more accurately, and the patient's disease progression can be dynamically tracked. Especially during the treatment process, the changes in lesion volume can be regularly evaluated to help doctors make more accurate judgments.

[0032] Furthermore, nonlinear differential equations were used to determine the lesion volume growth rate and change rate, including, The volume change data of the lesion is fitted using a nonlinear differential equation, which is expressed as: ;in represents the lesion volume at time t, represents the growth rate of the lesion volume, Indicates the maximum limit value of the lesion volume; Using historical data and using the mean square error (MSE) as the loss function, the difference between the observed volume and the predicted volume is minimized; Use nonlinear least squares method to iteratively calculate the loss. If the loss of the model no longer decreases significantly during the continuous iteration, the iteration is stopped and the final K and ; The rate of change of the growth rate was determined based on the change in the lesion volume growth rate at different times, and the rate of change was evaluated based on the physician's experience.

[0033] By utilizing historical lesion volume data and fitting model parameters using the nonlinear least squares method, the volume changes of lesions in the future can be accurately predicted. This can help doctors predict the development trend of lesions, facilitate the formulation of intervention measures in advance, and evaluate the expansion or contraction of lesions in the future, thereby achieving early warning of the disease, reducing patients' risks, and improving prognosis.

[0034] Example 2, reference Figure 2, which is a second embodiment of the present invention, is different from the previous embodiment and provides a system for tracking a patient's condition, including: An image data processing module is used to collect the patient's lesion image data and perform image preprocessing; Rigid registration module, used for rigid registration through affine transformation; Lesion recognition and segmentation module, which is used to predict the probability distribution of lesion areas based on deep convolutional neural networks and use conditional random fields for lesion segmentation and labeling; The non-rigid registration module is used to describe the local displacement through the B-spline basis function and perform non-rigid registration; The lesion volume calculation module is used to select the lesion area for volume calculation based on the image data after non-rigid registration; The volume change analysis module is used to determine the volume growth rate and change rate of the lesion using nonlinear differential equations.

[0035] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0036] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0037] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0038] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0039] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Patient condition tracking method, It is characterized in that include: Collect the patient's lesion image data, and perform rigid registration on the lesion image data through affine transformation; Predict lesion probability distribution based on deep convolutional neural network, and segment and mark lesions based on conditional random field (CRF); The B-spline basis function is used to describe the local displacement to perform non-rigid registration on the image data of the lesion area to generate the final image; The lesion area was selected for volume calculation, and the lesion volume growth rate and change rate were determined using nonlinear differential equations.

2. The method for tracking a patient's condition as claimed in claim 1, It is characterized in that The collecting of patient lesion image data comprises: Collect image data according to the patient's specific condition needs; Determine the frequency of image data collection based on physician experience; Preprocessing is performed based on the image data, including image data denoising, grayscale standardization, and image cropping.

3. The patient condition tracking method according to claim 2, It is characterized in that The rigid registration of the lesion image data by affine transformation comprises: Based on the preprocessed image data, the image data of multiple time points are preliminarily aligned through affine transformation, and the affine transformation matrix is ​​defined. Through matrix multiplication and vector addition operations, the new position of each pixel is calculated for rigid registration, which is expressed as: in Indicates the changed coordinates of the pixel coordinate point. Represents the pixel coordinates of the original image, and b are the affine transformation matrix and displacement vector respectively; Determine the reference image based on the training set, select the mean square error MSE as the similarity measurement function, the goal is to minimize the difference between the target image and the reference image, use the Adam optimizer and the gradient descent method to perform iterative optimization, and stop the iteration when the loss calculated in the continuous iteration process no longer decreases significantly, determine the optimal A and b, and output the optimal affine transformation matrix; Through rigid registration, the coordinates of each pixel are transformed to the new position coordinates , generate aligned images.

4. The method for tracking the patient's condition as claimed in claim 3, It is characterized in that The method predicts the probability distribution of lesions based on a deep convolutional neural network and performs lesion segmentation and labeling based on a conditional random field (CRF), including: A lesion area model is constructed based on a deep convolutional neural network, including an input layer, a convolutional layer, a maximum pooling layer, a fully connected layer, and an output layer; Use the training set to train the model, and select the cross entropy loss function to calculate the difference between the model prediction result and the actual label of the lesion area. Use the Adam optimizer to perform gradient descent optimization and update the model parameters. If the model loss no longer decreases significantly during the continuous iteration process, stop iterating and output the model. A lesion probability distribution map based on the aligned image is obtained based on the lesion region segmentation prediction result output by the lesion region model; Based on the conditional random field CRF, the probability of each pixel belonging to the lesion area is taken as a single-point term, the similarity between pixels is defined as a double-point term, and the energy function is defined based on the single-point term and the double-point term, which is expressed as: ; ; ;in Indicates the cost of the i-th pixel belonging to the lesion, represents the probability that the i-th pixel belongs to the lesion area, represents the similarity cost between the adjacent i-th pixel and j-th pixel, and Represent the spatial coordinates of the i-th pixel and the j-th pixel respectively, represents the weight coefficient, and Represent the grayscale values ​​of the i-th pixel and the j-th pixel respectively, and denote the spatial scale parameter and the color scale parameter respectively, Represents the total energy function value, N represents the total number of pixels, Represents the neighboring pixel area of ​​the i-th pixel; Use the training set and select the cross entropy loss function to calculate the loss between the lesion prediction results of the conditional random field CRF and the actual label, use the Adam optimizer for gradient descent optimization, and update the conditional random field CRF parameters, including , and , if the calculated loss no longer decreases significantly during the continuous iteration process, the iteration is stopped, the model parameters are output, and the model is updated; The mean-field approximation method is used to optimize the energy function and determine the optimization formula, which is expressed as: ;in represents the normalization factor, represents the classification probability, Represents the classification probability of adjacent pixels; The predicted probability map of the lesion area model is used as the initial ,Sure ; based on The probability distribution of each pixel is updated in the continuous iteration process. If the change in the value of is no longer significantly reduced, the iteration is stopped and the final output of the model is ; Based on the final Generate the corresponding lesion area probability map, segment the pixel values ​​of the probability map based on the historical average value as the segmentation threshold, and mark the lesion area.

5. The method for tracking the patient's condition as claimed in claim 4, It is characterized in that The method uses the B-spline basis function to describe the local displacement and non-rigidly registers the image data of the lesion area to generate the final image, including: Generate a grid based on the lesion area of ​​the image data, define the grid nodes as control points, and perform non-rigid registration; Based on each control point, the B-spline basis function is used to describe the local displacement, which is expressed as: ; ;in represents the displacement of pixel point x, n represents the number of control points adjacent to the pixel point, represents the displacement vector of control point i, represents the B-spline basis function, represents the coordinates of the ith control point, represents the third-order B-spline basis function; The reference image is determined based on the training set, and the mean square error (MSE) is selected as the similarity measurement function. The goal is to minimize the difference between the target image and the reference image, which is expressed as: ;in represents the non-rigid registration calculation error, Represents the total number of pixels. represents the coordinates of the i-th pixel, is the corresponding pixel coordinate in the reference image, M represents the total number of control points, represents the smoothness regularization parameter, which is determined based on historical data, represents the displacement vector gradient of control point j, Represents pixel The displacement of Use the Levenberg-Marquardt optimization algorithm to calculate the displacement of the current control point , calculate the value of the non-rigid registration calculation error, and calculate the displacement of each control point by the objective function The gradient of the control point is then updated. When the calculated error no longer decreases significantly during the continuous optimization process, the iteration is stopped and the displacement of the control point is optimized. ; Calculate pixel displacement based on control point displacement through non-rigid registration , and further adjust the pixel positions based on the aligned images to generate the final image.

6. The method for tracking the patient's condition as claimed in claim 5, It is characterized in that The step of selecting a lesion area for volume calculation includes: Based on the image scanning equipment, three-dimensional image data is formed by stacking multiple layers of image data. Based on the final image generated after non-rigid registration, the lesion change area is determined and the voxels in the lesion change area are selected for volume calculation.

7. The method for tracking the patient's condition as claimed in claim 6, It is characterized in that The method of using a nonlinear differential equation to determine the growth rate and change rate of the lesion volume comprises: The volume change data of the lesion is fitted using a nonlinear differential equation, expressed as: ;in represents the lesion volume at time t, represents the growth rate of the lesion volume, Indicates the maximum limit value of the lesion volume; Using historical data and using the squared error as the loss function, the difference between the observed volume and the predicted volume is minimized; The loss is calculated iteratively using the nonlinear least squares method. If the model loss no longer decreases significantly during the continuous iteration, the iteration is stopped and the final K and ; The rate of change of the growth rate was determined based on the change in the lesion volume growth rate at different times, and the rate of change was evaluated based on the physician's experience.

8. A system based on the patient condition tracking method according to any one of claims 1 to 7, It is characterized in that include: An image data processing module is used to collect the patient's lesion image data and perform image preprocessing; Rigid registration module, used for rigid registration through affine transformation; Lesion recognition and segmentation module, which is used to predict the probability distribution of lesion areas based on deep convolutional neural networks and use conditional random fields to segment and mark lesions; The non-rigid registration module is used to describe the local displacement through the B-spline basis function and perform non-rigid registration; A lesion volume calculation module is used to select the lesion area for volume calculation based on the image data after non-rigid registration; The volume change analysis module is used to determine the volume growth rate and change rate of the lesion using nonlinear differential equations.

9. A computer device, include: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of the patient condition tracking method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the patient condition tracking method according to any one of claims 1 to 7 are implemented.

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