Patient tumor monitoring system based on stereoscopic image reconstruction

Abstract

The invention discloses a patient tumor monitoring system based on stereoscopic image reconstruction, relates to the technical field of tumor monitoring, and discloses a stereoscopic image data analysis module, a monitoring actual influence analysis module, a monitoring simulation influence analysis module and a patient tumor monitoring adjustment module. A three-dimensional image data analysis module and a monitoring actual influence analysis module are arranged, analysis of various types of three-dimensional image data is carried out on a patient tumor, a deep learning mode is combined to carry out accurate evaluation on the patient tumor, a monitoring simulation influence analysis module and a patient tumor monitoring adjustment module are arranged, and a tumor three-dimensional image simulation model is constructed to carry out accurate evaluation on the patient tumor. The patient tumor is simulated and analyzed, the influence degree of the patient tumor on the monitoring standard is comprehensively analyzed from the two aspects of actual influence and simulated influence, and then the monitoring strategy for the patient tumor is accurately and quantitatively adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of tumor monitoring, and more specifically, to a patient tumor monitoring system based on stereoscopic image reconstruction. Background Art

[0002] In the field of tumor treatment, continuous and accurate monitoring of patients' tumors is the key to ensuring treatment effectiveness and the quality of life of patients. Traditional tumor monitoring methods mainly rely on doctors' clinical experience, regular imaging examinations, and tumor marker detection, but these methods have many limitations. For example, doctors' clinical experience may be affected by subjective factors, regular imaging examinations may not capture subtle changes in tumor growth, and tumor marker detection may be interfered with by a variety of physiological and pathological factors.

[0003] With the rapid development of medical imaging technology, stereoscopic imaging technologies such as CT (computed tomography), MRI (magnetic resonance imaging) and PET (positron emission tomography) have become important tools for tumor diagnosis and treatment. These technologies can provide three-dimensional structural and functional information of tumors, providing doctors with more intuitive and accurate tumor images. However, relying solely on these stereoscopic imaging data for tumor monitoring still has certain challenges, because the growth and spread of tumors is a complex process that requires comprehensive consideration of multiple factors.

[0004] Therefore, in order to overcome the limitations of traditional tumor monitoring methods and improve the accuracy and efficiency of tumor monitoring, the present invention proposes a patient tumor monitoring system based on stereoscopic image reconstruction. Summary of the invention

[0005] In view of the deficiencies in the prior art, an object of the present invention is to provide a patient tumor monitoring system based on stereoscopic image reconstruction.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A patient tumor monitoring system based on stereoscopic image reconstruction, comprising a stereoscopic image data analysis module, a monitoring actual impact analysis module, a monitoring simulation impact analysis module, and a patient tumor monitoring adjustment module;

[0008] The stereoscopic image data analysis module collects various types of stereoscopic image data of the patient's tumor every time a tumor image reconstruction time T passes, obtains basic data values ​​of various types of stereoscopic image data, and determines whether to mark the stereoscopic image data as abnormal image data based on a comparison result between the basic data value and the basic data threshold;

[0009] The monitoring actual impact analysis module obtains the monitoring actual impact value based on the abnormal image data;

[0010] The monitoring simulation impact analysis module builds a tumor stereoscopic image simulation model based on various types of stereoscopic image data of the patient's tumor and historical data of the patient's tumor, and obtains a monitoring simulation impact value based on the tumor stereoscopic image simulation model;

[0011] The patient tumor monitoring adjustment module obtains a monitoring adjustment value based on the monitoring actual impact value and the monitoring simulation impact value, and determines whether to adjust the monitoring standard for the patient tumor based on a comparison result between the monitoring adjustment value and the monitoring adjustment threshold.

[0012] Furthermore, the basic data values ​​of each type of stereoscopic image data are obtained as follows: obtain the basic data model corresponding to each type of stereoscopic image data, extract data features for each type of stereoscopic image data, obtain feature data of each type of stereoscopic image data after extraction, input the feature data of each type of stereoscopic image data into the corresponding basic data model, and then output the basic data values ​​of each type of stereoscopic image data.

[0013] Furthermore, the actual impact value of monitoring is obtained based on the abnormal image data. Specifically, the total number of abnormal image data is marked as Hrw, all abnormal image data are matched in pairs, the basic data values ​​of the two matched abnormal image data are summed up to obtain the abnormal data accumulation value, and the abnormal data accumulation threshold is set. When the abnormal data accumulation value is greater than or equal to the abnormal data accumulation threshold, the abnormal accumulation times are increased by one, and the abnormal accumulation times are marked as Zry. The formula is used The actual monitoring impact value DPX is obtained, where b1 is the coefficient of the number of abnormal data and b2 is the coefficient of the cumulative number of abnormalities.

[0014] Furthermore, the monitoring simulation impact value is obtained based on the tumor stereoscopic image simulation model, specifically: the tumor stereoscopic image simulation model simulates the change of the tumor image reconstruction time T, sets J tumor simulation time points with equal time intervals within the tumor image reconstruction time T, obtains the simulated performance value Sjk of each tumor simulation time point, j = 1, 2, ..., J, j is the sequence number of the tumor simulation time point, sets the simulation performance coefficient to Bk, obtains the number of simulated loss periods Rgs and the number of simulated performance fluctuations Lzp, and uses the formula The monitoring simulation impact value MZE is obtained.

[0015] Furthermore, the number of simulated lost periods Rgs and the number of simulated performance fluctuations Lzp are obtained as follows: all simulated performance values ​​are sorted in sequence according to the chronological order of the tumor simulation time points, the two adjacent simulated performance values ​​after sorting are summed to obtain the period performance value, a period performance threshold is set, and when the period performance value is greater than or equal to the period performance threshold, the number of simulated lost periods is increased by one, and the number of simulated lost periods is marked as Rgs, the difference between the two adjacent simulated performance values ​​after sorting is calculated and the absolute value is taken to obtain the simulated performance fluctuation value, and a simulated performance fluctuation threshold is set, and when the simulated performance fluctuation value is greater than or equal to the simulated performance fluctuation threshold, the number of simulated performance fluctuations is increased by one, and the number of simulated performance fluctuations is marked as Lzp.

[0016] Furthermore, the simulation performance value at the tumor simulation time point is obtained as follows: the basic data values ​​of each type of stereoscopic image data when the tumor stereoscopic image simulation model is at the tumor simulation time point are collected, the basic data values ​​of all types of stereoscopic image data are summed up and averaged to obtain the simulation performance value at the tumor simulation time point.

[0017] Furthermore, the monitoring adjustment value is obtained based on the monitoring actual impact value and the monitoring simulation impact value, specifically: using the formula The monitoring adjustment value SFUP is obtained, where wa is the actual influence coefficient and wb is the simulated influence coefficient.

[0018] Furthermore, based on the comparison result between the monitoring adjustment value and the monitoring adjustment threshold, it is determined whether to adjust the monitoring standard of the patient's tumor. Specifically, the monitoring adjustment threshold is set to YZUP. When the monitoring adjustment value SFUP is greater than the monitoring adjustment threshold YZUP, after the tumor image reconstruction time Collect various types of stereoscopic image data of the patient's tumor next time. When the monitoring adjustment value SFUP is less than or equal to the monitoring adjustment threshold YZUP, keep the tumor image reconstruction time T and collect various types of stereoscopic image data of the patient's tumor next time.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. Set up a stereoscopic image data analysis module and a monitoring actual impact analysis module to analyze various types of stereoscopic image data of patient tumors, and accurately evaluate patient tumors by combining deep learning methods;

[0021] 2. Set up a monitoring simulation impact analysis module and a patient tumor monitoring adjustment module. By constructing a tumor stereoscopic image simulation model, simulate and analyze the patient's tumor. Comprehensively analyze the impact of the patient's tumor on the monitoring standard from both actual impact and simulated impact, and then accurately quantify and adjust the monitoring strategy for the patient's tumor. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a system module diagram of a patient tumor monitoring system based on stereoscopic image reconstruction;

[0023] Figure 2 A system operation flow chart of a patient tumor monitoring system based on stereoscopic image reconstruction;

[0024] Figure 3 The figure is a flowchart for obtaining the basic data values ​​of stereoscopic image data. DETAILED DESCRIPTION

[0025] Reference Figure 1-Figure 3 , a patient tumor monitoring system based on stereoscopic image reconstruction, including a stereoscopic image data analysis module, a monitoring actual impact analysis module, a monitoring simulation impact analysis module, and a patient tumor monitoring adjustment module.

[0026] Stereoscopic image data analysis module: Set the tumor image reconstruction time to T (the length of the tumor image reconstruction time T is set according to the requirements of the monitoring system), and collect various types of stereoscopic image data of the patient's tumor after each tumor image reconstruction time T (the types of stereoscopic image data of the patient's tumor include CT data, MRI data, MRA data, MRV data, PET data, SPEC data, etc.), obtain the basic data value of each type of stereoscopic image data, and set the basic data threshold (the basic data threshold is a system preset value). When the basic data value of the stereoscopic image data is greater than or equal to the basic data threshold, the stereoscopic image data is marked as abnormal image data, and when the basic data value of the stereoscopic image data is less than the basic data threshold, no processing is performed.

[0027] The method for obtaining the basic data values ​​of each type of stereoscopic image data is as follows: obtain the basic data model corresponding to each type of stereoscopic image data, extract data features for each type of stereoscopic image data, obtain feature data of each type of stereoscopic image data after extraction, input the feature data of each type of stereoscopic image data into the corresponding basic data model respectively, and then output the basic data values ​​of each type of stereoscopic image data.

[0028] Different types of stereoscopic image data correspond to different basic data models. For example, CT data and MRI data correspond to a basic data model respectively. Each basic data model is constructed based on a deep learning model. The difference in the construction process of different basic data models lies in the difference in training data. The following will introduce the construction method of the basic data model of CT data: collect n groups of CT data, extract data features for each group of CT data, and then obtain feature data of n groups of CT data, build a deep learning model, use the feature data of CT data as training data of the deep learning model, assign a basic data value to each training data, and the value range of the basic data value is (3.0~6.0). The larger the value of the basic data value, the more abnormal the CT data of the patient's tumor is, and the smaller the value of the basic data value, the more normal the CT data of the patient's tumor is. The training data is divided into a training set, a validation set, and a test set according to a set ratio of 3:1:1, and the training set, validation set, and test set are trained. After the training is completed, the basic data model of CT data is obtained.

[0029] Monitoring actual impact analysis module: mark the total number of abnormal image data as Hrw, match all abnormal image data in pairs, sum the basic data values ​​of the two abnormal image data after matching, get the abnormal data accumulation value, set the abnormal data accumulation threshold (the abnormal data accumulation threshold is the system preset value), when the abnormal data accumulation value is greater than or equal to the abnormal data accumulation threshold, increase the abnormal accumulation times by one, when the abnormal data accumulation value is less than the abnormal data accumulation threshold, do not process it, mark the abnormal accumulation times as Zry, and use the formula The actual monitoring impact value DPX is obtained, where b1 is the coefficient of the number of abnormal data, b2 is the coefficient of the cumulative number of abnormalities, the value of b1 is 0.92, and the value of b2 is 0.88.

[0030] A stereoscopic image data analysis module and a monitoring actual impact analysis module are set up to analyze various types of stereoscopic image data of patient tumors, and a deep learning method is used to accurately evaluate patient tumors.

[0031] Monitoring simulation impact analysis module: A tumor stereoscopic image simulation model is constructed based on various types of stereoscopic image data of the patient's tumor and the historical data of the patient's tumor (the historical data of the patient's tumor is the historical data of various types of stereoscopic image data of the patient's tumor). The tumor stereoscopic image simulation model simulates the change of the tumor image reconstruction time T, sets J tumor simulation time points with equal time intervals within the tumor image reconstruction time T, obtains the simulated performance value Sjk of each tumor simulation time point, j=1, 2, ..., J, j is the sequence number of the tumor simulation time point, sets the simulation performance coefficient to Bk, k=1, 2, ..., k, B1<B2<B3<...<Bk, sets each simulation performance coefficient to correspond to a simulation performance value within a range, and the range of the simulation performance value includes (0, Sj1], (Sj1, Sj2], ..., (Sjk-1, Sjk], when Sjk∈(Sj1, Sj2], the simulation performance The current coefficient is B2, all simulated performance values ​​are sorted in sequence according to the sequence of tumor simulation time points, the two adjacent simulated performance values ​​after sorting are summed to obtain the time period performance value, and the time period performance threshold is set (the time period performance threshold is the system preset value). When the time period performance value is greater than or equal to the time period performance threshold, the number of simulated lost time periods is increased by one. When the time period performance value is less than the time period performance threshold, no processing is performed, and the number of simulated lost time periods is marked as Rgs. The difference between the two adjacent simulated performance values ​​after sorting is calculated and the absolute value is taken to obtain the simulated performance fluctuation value, and the simulated performance fluctuation threshold is set (the simulated performance fluctuation threshold is the system preset value). When the simulated performance fluctuation value is greater than or equal to the simulated performance fluctuation threshold, the number of simulated performance fluctuations is increased by one. When the simulated performance fluctuation value is less than the simulated performance fluctuation threshold, no processing is performed, and the number of simulated performance fluctuations is marked as Lzp. Using the formula The monitoring simulation impact value MZE is obtained.

[0032] The method for obtaining the simulated performance value at the tumor simulation time point is as follows: collect the basic data values ​​of each type of stereoscopic image data when the tumor stereoscopic image simulation model is at the tumor simulation time point, sum and average the basic data values ​​of all types of stereoscopic image data, and obtain the simulated performance value at the tumor simulation time point.

[0033] The method for constructing a tumor stereoscopic imaging simulation model is as follows: select simulation software such as Materialise M imics and Amira, create a new tumor model in the simulation software based on various types of stereoscopic imaging data of the patient's tumor, and optimize various parameters of the tumor model, determine the growth trend and diffusion rate of the tumor model based on the historical data of the patient's tumor, determine the change direction of the tumor model based on the growth trend and diffusion rate, and then construct a tumor stereoscopic imaging simulation model, which will simulate changes in the direction of change.

[0034] Patient Tumor Monitoring Adjustment Module: Using Formula The monitoring adjustment value SFUP is obtained, where wa is the actual influence coefficient, wb is the simulated influence coefficient, wa is 0.52, wb is 0.43, and the monitoring adjustment threshold is set to YZUP. When the monitoring adjustment value SFUP is greater than the monitoring adjustment threshold YZUP, after the tumor image reconstruction time Collect various types of stereoscopic image data of the patient's tumor next time. When the monitoring adjustment value SFUP is less than or equal to the monitoring adjustment threshold YZUP, keep the tumor image reconstruction time T and collect various types of stereoscopic image data of the patient's tumor next time.

[0035] A monitoring simulation impact analysis module and a patient tumor monitoring adjustment module are set up. By constructing a tumor stereoscopic image simulation model, a simulation analysis of the patient's tumor is performed. The degree of influence of the patient's tumor on the monitoring standard is comprehensively analyzed from both actual impact and simulated impact, and then the monitoring strategy for the patient's tumor is accurately quantified and adjusted.

[0036] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0037] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0038] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0039] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0040] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0041] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0042] If the functions are implemented in the form of 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 application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0043] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A patient tumor monitoring system based on stereoscopic image reconstruction, characterized in that: It includes stereoscopic image data analysis module, monitoring actual impact analysis module, monitoring simulation impact analysis module, and patient tumor monitoring adjustment module; The stereoscopic image data analysis module collects various types of stereoscopic image data of the patient's tumor every time a tumor image reconstruction time T passes, obtains basic data values ​​of various types of stereoscopic image data, and determines whether to mark the stereoscopic image data as abnormal image data based on a comparison result between the basic data value and the basic data threshold; The monitoring actual impact analysis module obtains the monitoring actual impact value based on the abnormal image data; The monitoring simulation impact analysis module builds a tumor stereoscopic image simulation model based on various types of stereoscopic image data of the patient's tumor and historical data of the patient's tumor, and obtains a monitoring simulation impact value based on the tumor stereoscopic image simulation model; The patient tumor monitoring adjustment module obtains a monitoring adjustment value based on the monitoring actual impact value and the monitoring simulation impact value, and determines whether to adjust the monitoring standard for the patient tumor based on a comparison result between the monitoring adjustment value and the monitoring adjustment threshold.

2. A patient tumor monitoring system based on stereoscopic image reconstruction according to claim 1, characterized in that: The method for obtaining the basic data values ​​of each type of stereoscopic image data is as follows: obtain the basic data model corresponding to each type of stereoscopic image data, extract data features for each type of stereoscopic image data, obtain feature data of each type of stereoscopic image data after extraction, input the feature data of each type of stereoscopic image data into the corresponding basic data model respectively, and then output the basic data values ​​of each type of stereoscopic image data.

3. The patient tumor monitoring system based on stereoscopic image reconstruction according to claim 1, characterized in that: The actual impact value of monitoring is obtained based on abnormal image data. Specifically, the total number of abnormal image data is marked as Hrw, all abnormal image data are matched in pairs, and the basic data values ​​of the two abnormal image data after matching are summed to obtain the abnormal data accumulation value. The abnormal data accumulation threshold is set. When the abnormal data accumulation value is greater than or equal to the abnormal data accumulation threshold, the abnormal accumulation times are increased once and marked as Zry. The formula is used The actual monitoring impact value DPX is obtained, where b1 is the coefficient of the number of abnormal data and b2 is the coefficient of the cumulative number of abnormalities.

4. The patient tumor monitoring system based on stereoscopic image reconstruction according to claim 1, characterized in that: The monitoring simulation impact value is obtained based on the tumor stereoscopic image simulation model, specifically: the tumor stereoscopic image simulation model simulates the changes in the tumor image reconstruction time T, sets J tumor simulation time points with equal time intervals within the tumor image reconstruction time T, obtains the simulated performance value Sjk of each tumor simulation time point, j = 1, 2, ..., J, j is the sequence number of the tumor simulation time point, sets the simulation performance coefficient to Bk, obtains the number of simulated loss periods Rgs and the number of simulated performance fluctuations Lzp, and uses the formula The monitoring simulation impact value MZE is obtained.

5. The patient tumor monitoring system based on stereoscopic image reconstruction according to claim 4, characterized in that: The number of simulated lost periods Rgs and the number of simulated performance fluctuations Lzp are obtained as follows: all simulated performance values ​​are sorted in sequence according to the chronological order of the tumor simulation time points, the two adjacent simulated performance values ​​after sorting are summed to obtain the period performance value, a period performance threshold is set, when the period performance value is greater than or equal to the period performance threshold, the number of simulated lost periods is increased by one, the number of simulated lost periods is marked as Rgs, the difference between the two adjacent simulated performance values ​​after sorting is calculated and the absolute value is taken to obtain the simulated performance fluctuation value, the simulated performance fluctuation threshold is set, when the simulated performance fluctuation value is greater than or equal to the simulated performance fluctuation threshold, the number of simulated performance fluctuations is increased by one, and the number of simulated performance fluctuations is marked as Lzp.

6. The patient tumor monitoring system based on stereoscopic image reconstruction according to claim 4, characterized in that: The method for obtaining the simulated performance value at the tumor simulation time point is as follows: collect the basic data values ​​of each type of stereoscopic image data when the tumor stereoscopic image simulation model is at the tumor simulation time point, sum and average the basic data values ​​of all types of stereoscopic image data, and obtain the simulated performance value at the tumor simulation time point.

7. The patient tumor monitoring system based on stereoscopic image reconstruction according to claim 1, characterized in that: The monitoring adjustment value is obtained based on the actual monitoring impact value and the monitoring simulation impact value. Specifically, the formula is: The monitoring adjustment value SFUP is obtained, where wa is the actual influence coefficient and wb is the simulated influence coefficient.

8. The patient tumor monitoring system based on stereoscopic image reconstruction according to claim 1, characterized in that: Based on the comparison result between the monitoring adjustment value and the monitoring adjustment threshold, it is determined whether to adjust the monitoring standard of the patient's tumor. Specifically, the monitoring adjustment threshold is set to YZUP. When the monitoring adjustment value SFUP is greater than the monitoring adjustment threshold YZUP, after the tumor image reconstruction time Collect various types of stereoscopic image data of the patient's tumor next time. When the monitoring adjustment value SFUP is less than or equal to the monitoring adjustment threshold YZUP, keep the tumor image reconstruction time T and collect various types of stereoscopic image data of the patient's tumor next time.

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