Spinal function evaluation method, device and equipment based on image analysis and medium

Abstract

The embodiment of the invention relates to the technical field of intelligent medical treatment, and discloses a spine function evaluation method and device based on image analysis, equipment and a medium. The method comprises the steps of obtaining a spine image sequence of a user; performing spine key point identification on the spine image sequence by using a pre-trained spine function identification model to obtain spine key point information, and performing feature engineering feature calculation on the spine key point information to obtain a spine state feature set; performing a full-connection feature recognition operation based on a preset target spine function type on the spine state feature set to obtain scores of key factors corresponding to the target spine function type, and according to a preset evaluation weight of each key factor on the target spine function type, obtaining a score of each key factor corresponding to the target spine function type; and performing weighted summation calculation on the scores to obtain an evaluation score of the user in the aspect of the target spine function type. By means of the scheme, the accuracy of spinal function evaluation can be improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of smart medical technology, and in particular to a spinal function assessment method, device, equipment and medium based on image analysis. Background Art

[0002] In existing medical technologies, the assessment of spinal function mainly relies on spinal scanning images to achieve spinal status assessment. However, due to the influence of scanning technology, the spine is scanned multiple times and for a long time, which can easily cause damage to the patient's body cells during the scanning process.

[0003] In addition, traditional examination methods cannot record the limits of the user's spinal changes in an objective and scientific way. They are often affected by the personal experience and skill level of the observer of the spinal image, and the evaluation results may be subjective and inconsistent. Today's spinal status assessment equipment cannot always obtain spinal images of patients during exercise, which makes it impossible to objectively and accurately assess the true state of the spine. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a spinal function assessment method, device, electronic device and storage medium based on image analysis, which can improve the accuracy of spinal function assessment.

[0005] In order to solve the above technical problems, one embodiment of the present invention provides a spinal function assessment method based on image analysis, the method comprising:

[0006] Obtaining a spine image sequence of a user;

[0007] Using a pre-trained spinal function recognition model, the spinal key point recognition is performed on the spinal image sequence to obtain spinal key point information, and feature engineering feature calculation is performed on the spinal key point information to obtain a spinal state feature set;

[0008] A fully connected feature recognition operation based on a preset target spinal function type is performed on the spinal status feature set to obtain scores for each key factor corresponding to the target spinal function type, and a weighted sum calculation is performed on each score based on the preset evaluation weights of the target spinal function type for each key factor to obtain an evaluation score of the user for the target spinal function type.

[0009] Another embodiment of the present invention also provides a spinal function assessment device based on image analysis, the device comprising:

[0010] A user spine scanning module, used to obtain a user's spine image sequence;

[0011] A spine feature extraction module is used to use a pre-trained spine function recognition model to perform spine key point recognition on the spine image sequence to obtain spine key point information, and perform feature engineering feature calculation on the spine key point information to obtain a spine state feature set;

[0012] A spinal function assessment module is used to perform a fully connected feature recognition operation on the spinal status feature set based on a preset target spinal function type, obtain the score of each key factor corresponding to the target spinal function type, and perform a weighted sum calculation on each score based on the preset assessment weights of the target spinal function type for each key factor, so as to obtain the user's assessment score for the target spinal function type.

[0013] Other embodiments of the present invention also provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above-mentioned image analysis-based spinal function assessment method.

[0014] Other embodiments of the present invention further provide a computer-readable storage medium storing a computer program, which implements the above-mentioned spinal function assessment method based on image analysis when executed by a processor.

[0015] In the embodiment of the present invention, computer vision intelligent technology and algorithms are introduced to achieve measurement and analysis of the user's spinal movement, thereby avoiding the influence of the doctor's subjective experience on the evaluation result.

[0016] In addition, the step of obtaining a spine image sequence of a user includes:

[0017] Acquire a sequence of motion images when the user performs a preset standard detection action;

[0018] The motion image sequence is subjected to spinal morphology prediction and recognition to obtain spinal prediction results corresponding to each extreme position action image in the motion image sequence, and each spinal prediction result is arranged to obtain a spinal image sequence.

[0019] In addition, the step of performing spine morphology prediction and recognition on the motion image sequence to obtain spine prediction results corresponding to each extreme position action image in the motion image sequence includes:

[0020] Performing a feature extraction operation based on human outline and human posture on the motion image sequence to obtain human outline features and human posture features;

[0021] A spine posture prediction operation is performed on the human body contour features and human body posture features to obtain a spine prediction result sequence corresponding to the motion image sequence, and spine prediction results corresponding to each extreme position action image in the motion image sequence are extracted from the spine prediction result sequence.

[0022] In addition, after obtaining the spine prediction results corresponding to each extreme position action image in the motion image sequence, the method includes:

[0023] Performing an image denoising operation on the spine prediction result using a Gaussian algorithm to obtain a denoised image, and performing a contrast enhancement operation on the denoised image using a histogram equalization algorithm to obtain a contrast enhanced image;

[0024] The contrast enhanced image is subjected to spine detection and interception operations using an edge detection algorithm to obtain an image enhanced spine prediction result.

[0025] In addition, before using the pre-trained spinal function recognition model, the method further includes:

[0026] Obtain a pre-built spinal function recognition model and a training sample set based on clinical statistics;

[0027] Extracting a target training sample from the training sample set in turn, performing feature extraction operation on the target training sample using the spinal function recognition model, and performing random forest classification judgment on the feature extraction result to obtain a predicted spinal assessment result;

[0028] According to the random forest algorithm, the correlation score of each feature in the feature extraction result to the predicted spinal assessment result is obtained, and according to the arrangement result of each correlation score, the key features in the feature extraction result are obtained;

[0029] Calculating the loss value between the predicted spine assessment result and the true label of the target training sample using a cross entropy loss algorithm;

[0030] Calculate the minimization of the loss value to obtain model parameters, and perform a network back-propagation operation on the spinal function recognition model according to the model parameters to obtain an updated spinal function recognition model;

[0031] Determining the convergence of the loss value;

[0032] When the loss value does not converge, return to the above step of extracting a target training sample from the training sample set in sequence, and train the spinal function recognition model for spinal status assessment based on the key features;

[0033] When the loss value converges, the training process is stopped to obtain a trained spinal function recognition model.

[0034] In addition, the feature engineering feature calculation is performed on the spine key point information to obtain a spine state feature set, including:

[0035] Acquire key features in the spinal function recognition model, and perform secondary construction on each of the key features based on preset feature engineering to obtain secondary features;

[0036] According to the weight configuration rules in the preset feature engineering, the key features and the secondary features are weighted to obtain a spinal state feature set.

[0037] In addition, after obtaining the user's spine image sequence, the method further includes:

[0038] Obtain the user's default lying posture and real spine image;

[0039] Performing spinal feature recognition on the real spinal image to obtain a real spinal feature set;

[0040] Extracting a target spinal image from the spinal image sequence in sequence, and acquiring a target motion image corresponding to the target spinal image;

[0041] Identifying motion change features of the target motion image and the default lying down motion;

[0042] According to the real spine feature set and the motion change feature, the target spine image is subjected to spine morphology correction to obtain a corrected spine image corresponding to the target motion image;

[0043] The corrected spinal images corresponding to the target spinal images in the spinal image sequence are acquired to obtain a spinal image sequence personalized and corrected by the user.

[0044] In the embodiment of the present invention, the spinal morphology is first predicted and identified to avoid long-term spinal scanning for the user, thereby improving the user's safety and greatly improving the efficiency of image acquisition; then, the pain questionnaire and the spinal image sequence are spliced ​​to identify the spinal state of the user during each standard detection action, thereby improving the efficiency of spinal state assessment and avoiding the risk of subjective recording and judgment by doctors. Therefore, the present invention provides a spinal function assessment method, device, electronic device and storage medium based on image analysis, which can improve the accuracy of spinal function assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] One or more embodiments of the present invention are exemplarily described by the images in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0046] Figure 1 is a flow chart of a spinal function assessment method based on image analysis according to an embodiment of the present invention;

[0047] Figure 2 is a schematic structural diagram of a spinal function assessment device based on image analysis according to another embodiment of the present invention;

[0048] Figure 3 is a schematic structural diagram of an electronic device provided according to another embodiment of the present invention. DETAILED DESCRIPTION

[0049] To make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the embodiments of the present invention, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation mode of the present embodiment should not constitute any limitation, and the various embodiments can be combined and referenced with each other without contradiction.

[0050] One embodiment of the present invention relates to a spinal function assessment method based on image analysis. In existing medical technologies, the assessment of a user's spinal function mainly relies on the doctor's clinical experience, the user's subjective description, and traditional physical examination methods. Although these methods can assess the user's condition to a certain extent, they have many shortcomings. First, traditional physical examination methods are often affected by the doctor's personal experience and skill level, and the assessment results may be subjective and inconsistent. Secondly, the user's subjective description is often affected by subjective feelings such as pain and fatigue, and it is difficult to accurately reflect their true spinal function status. In addition, existing assessment methods often lack continuous monitoring and recording of dynamic changes in spinal function, making it difficult to objectively evaluate the treatment effect.

[0051] To solve the above problems, the present invention provides a method for evaluating spinal function based on image analysis, such as Figure 1 As shown, the present invention specifically includes the following steps:

[0052] S1. Obtain a spine image sequence of a user.

[0053] In detail, in the embodiment of the present invention, the step of obtaining a sequence of spine images of a user includes:

[0054] Acquire a sequence of motion images when the user performs a preset standard detection action;

[0055] The motion image sequence is subjected to spinal morphology prediction and recognition to obtain spinal prediction results corresponding to each extreme position action image in the motion image sequence, and each spinal prediction result is arranged to obtain a spinal image sequence.

[0056] In the embodiment of the present invention, the standard detection action refers to the action of the user bending down, stretching backward, or twisting left and right under the instruction of the doctor.

[0057] In the embodiments of the present invention, a sequence of motion images of the user can be obtained by optical shooting equipment or equipment such as millimeter wave and microwave radars. The extreme position refers to a state where the movement reaches a certain limit and cannot continue. For example, when bending over to a certain degree and cannot bend over any further, a snapshot is taken at this time to obtain an extreme position action image.

[0058] In detail, in the embodiment of the present invention, the spine morphology prediction and recognition is performed on the motion image sequence to obtain the spine prediction results corresponding to each extreme position action image in the motion image sequence, including:

[0059] Performing a feature extraction operation based on human outline and human posture on the motion image sequence to obtain human outline features and human posture features;

[0060] A spine posture prediction operation is performed on the human body contour features and human body posture features to obtain a spine prediction result corresponding to the motion image sequence, and the spine prediction result corresponding to each extreme position action image in the motion image sequence is extracted.

[0061] In the embodiment of the present invention, the human figure contour and human body posture are realized by using a model pre-trained with a large number of samples to realize spine prediction.

[0062] Furthermore, in an embodiment of the present invention, after obtaining the spine prediction results corresponding to each extreme position action image in the motion image sequence, the method includes:

[0063] Performing an image denoising operation on the spine prediction result using a Gaussian algorithm to obtain a denoised image, and performing a contrast enhancement operation on the denoised image using a histogram equalization algorithm to obtain a contrast enhanced image;

[0064] The contrast enhanced image is subjected to spine detection and interception operations using an edge detection algorithm to obtain an image enhanced spine prediction result.

[0065] The Gaussian algorithm is a smoothing technique based on a Gaussian function, which is used to blur images and remove details and noise. The Gaussian function is a form of normal distribution, and its use in image processing helps to achieve a balance between retaining image edges and reducing noise.

[0066] The edge detection algorithm can identify points where image intensity (brightness) changes significantly, which usually correspond to changes in the outline of an object, the edge of a surface, or other visual attributes, thereby identifying the location of the boundaries of objects in the image.

[0067] The histogram equalization algorithm is a method for enhancing the contrast of an image by changing the histogram distribution of the image. By redistributing the gray levels of the image, the histogram (i.e., the frequency distribution diagram of the gray levels) becomes more uniform, thereby uniformly using all available gray levels in the entire image. The present invention can enhance the visual effect of the image through the histogram equalization algorithm, thereby improving the accuracy of intelligent evaluation.

[0068] The present invention optimizes the spine prediction result through the above-mentioned preprocessing operation, thereby improving the recognition accuracy of spine features in subsequent pictures.

[0069] In addition, in another embodiment of the present invention, after obtaining the spinal image sequence of the user, the method further includes:

[0070] Obtain the user's default lying posture and real spine image;

[0071] Performing spinal feature recognition on the real spinal image to obtain a real spinal feature set;

[0072] Extracting a target spinal image from the spinal image sequence in sequence, and acquiring a target motion image corresponding to the target spinal image;

[0073] Identifying motion change features of the target motion image and the default lying down motion;

[0074] According to the real spine feature set and the motion change feature, the target spine image is subjected to spine morphology correction to obtain a corrected spine image corresponding to the target motion image;

[0075] The corrected spinal images corresponding to the target spinal images in the spinal image sequence are acquired to obtain a spinal image sequence personalized and corrected by the user.

[0076] The real spinal image may be a magnetic resonance scan image, a CT scan image, etc. The default lying posture is the user's lying posture when the magnetic resonance scan image is obtained.

[0077] Although a pre-built spine morphology prediction model can be used for spine recognition (the spine morphology prediction model predicts the spine shape based on trunk movements, body fatness, etc.), there are differences between individuals, and the prediction results need to be personalized.

[0078] In addition, in another embodiment of the present invention, a pain questionnaire of the user when performing the standard detection action can also be obtained.

[0079] In another embodiment of the present invention, the user can record the process of performing the standard detection action to obtain a pain questionnaire.

[0080] The pain questionnaire includes pain type and pain level; the pain type includes tingling, dull pain and burning sensation; the pain level includes painless, mild, moderate, severe and extremely severe.

[0081] The pain questionnaire in the embodiment of the present invention can construct a key-value pair of answer number-option, where the answer number represents the standard detection action and the option is the pain type and intensity. In the embodiment of the present invention, quantization is performed by a one-hot encoding method to obtain a pain feature set.

[0082] The features of the pain questionnaire are then spliced ​​into the corresponding positions of the spinal image sequence to improve the richness of data features, thereby improving the accuracy of functional assessment of the subsequent spinal function recognition model.

[0083] S2. Using a pre-trained spinal function recognition model, the spinal key point recognition is performed on the spinal image sequence to obtain spinal key point information, and feature engineering feature calculation is performed on the spinal key point information to obtain a spinal state feature set.

[0084] In an embodiment of the present invention, the spinal function recognition model is a Transformer-based convolutional neural network, which is used to identify a targeted assessment score of the user's spinal status based on the spinal morphology.

[0085] Furthermore, in an embodiment of the present invention, before using the pre-trained spinal function recognition model, the method further includes:

[0086] Obtain a pre-built spinal function recognition model and a training sample set based on clinical statistics;

[0087] Extracting a target training sample from the training sample set in turn, performing feature extraction operation on the target training sample using the spinal function recognition model, and performing random forest classification judgment on the feature extraction result to obtain a predicted spinal assessment result;

[0088] According to the random forest algorithm, the correlation score of each feature in the feature extraction result to the predicted spinal assessment result is obtained, and according to the arrangement result of each correlation score, the key features in the feature extraction result are obtained;

[0089] Calculating the loss value between the predicted spine assessment result and the true label of the target training sample using a cross entropy loss algorithm;

[0090] Calculate the minimization of the loss value to obtain model parameters, and perform a network back-propagation operation on the spinal function recognition model according to the model parameters to obtain an updated spinal function recognition model;

[0091] Determining the convergence of the loss value;

[0092] When the loss value does not converge, return to the above step of extracting a target training sample from the training sample set in sequence, and train the spinal function recognition model for spinal status assessment based on the key features;

[0093] When the loss value converges, the training process is stopped to obtain a trained spinal function recognition model.

[0094] In the embodiment of the present invention, the training sample set is obtained from clinical medical statistics, including a user's spinal image sequence, pain questions and answers, and corresponding expert group evaluation results (true labels).

[0095] In the embodiment of the present invention, the training is performed by a cross entropy loss algorithm, and the training progress is controlled by the convergence of the loss value to obtain a trained spinal function recognition model. In addition, the present invention also uses a random forest algorithm to obtain the correlation score of each feature in the feature extraction result to the predicted spinal assessment result, thereby identifying key features in the spinal image that have an important impact on the preset target spinal function type (specific spinal state).

[0096] The random forest is a learning method based on decision tree ensemble, which makes the final decision by constructing multiple decision trees and aggregating their prediction results (e.g., by voting or averaging), and is usually used for feature selection and importance assessment. The cross entropy loss function is mainly used to evaluate the prediction results of classification problems, especially when the output is a probability distribution, to measure the difference between the actual label and the predicted label, with the goal of minimizing this difference.

[0097] In addition, during the model training process, feature engineering feature calculations need to be performed on the spinal key point information to obtain a spinal state feature set.

[0098] In detail, in the model training process of the embodiment of the present invention, the feature engineering feature calculation is performed on the spine key point information to obtain a spine state feature set, including:

[0099] Acquire key features in the spinal function recognition model, and perform secondary construction on each of the key features based on preset feature engineering to obtain secondary features;

[0100] According to the weight configuration rules in the preset feature engineering, the key features and the secondary features are weighted to obtain a spinal state feature set.

[0101] Specifically, when a key feature is identified, such as the vector (ab) between the midpoints of both hips (a) and the seventh cervical vertebra point (b), the key feature can be reconstructed according to the preset feature engineering to obtain a secondary feature, such as calculating the angle between the vector (ab) and the frontal plane of the human body.

[0102] Furthermore, after the model training is completed, the present invention obtains the average value of the correlation scores corresponding to each key feature in the trained model, and finally obtains the correlation score of each feature, thereby configuring the weight of the corresponding level to obtain a spinal state feature set, for example [key features (weight 1): midpoints of both hips, seventh cervical vertebra point, distal points of both hands fingers, unilateral wrist point...; and secondary features (weight 0.8): spinal flexion-overall, spinal flexion-distance from fingers to the ground, spinal extension, scoliosis-left / right, scoliosis-distance (L / R)... …], wherein the spinal flexion-overall refers to [the angle between the midpoints of both hips (a) and the seventh cervical vertebra point (b) is calculated by the vector (ab) with the frontal plane of the human body to determine the change in flexion angle during trunk flexion]; the spinal flexion-distance between fingers and the ground refers to [the distance between the distal points of the fingers of both hands and the ground is calculated respectively, and the average value is taken]; spinal extension refers to [the angle between the midpoints of both hips and the seventh cervical vertebra point is calculated by the vector with the frontal plane of the human body to determine the change in trunk activity angle during trunk extension] and other key features evaluated by the model, which will not be elaborated here.

[0103] S3. Perform a fully connected feature recognition operation based on a preset target spinal function type on the spinal state feature set to obtain scores for each key factor corresponding to the target spinal function type, and perform a weighted sum calculation on each score based on the preset evaluation weights of the target spinal function type for each key factor to obtain an evaluation score of the user in terms of the target spinal function type. In the embodiment of the present invention, the target spinal function type is a certain specific spinal state, such as ankylosing spondylosis, scoliosis, etc., and this application does not make specific restrictions on this. The various key factors may include spinal flexion, spinal extension, scoliosis, cervical lateral flexion, ankle joint spacing, etc., and this application does not make specific restrictions on this.

[0104] In the embodiment of the present invention, a spinal state assessment operation is implemented through a feature random forest full connection operation to obtain an assessment score, which can be expressed as a normalized score. The larger the score, the worse the spinal state, and vice versa.

[0105] In the embodiment of the present invention, the spinal morphology is first predicted and identified to avoid long-term spinal scanning for the user, thereby improving the user's safety and greatly improving the efficiency of image acquisition; then, the pain questionnaire and the spinal image sequence are spliced ​​to identify the spinal state of the user during each standard detection action, thereby improving the evaluation efficiency and avoiding the risk of subjective recording and judgment by the doctor. Therefore, the present invention provides a spinal function evaluation method based on image analysis, which can improve the accuracy of spinal function evaluation.

[0106] The above is a basic process. In actual applications, different methods and techniques may be used according to specific circumstances. It is worth noting that the field of image processing and computer vision is a complex and diverse field. It is necessary to select appropriate algorithms and techniques based on specific problems and application scenarios to achieve the extraction of key feature points of the spine.

[0107] The steps of the above method are divided only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0108] Another embodiment of the present invention relates to a spinal function assessment device based on image analysis. Figure 2 As shown, the spinal function assessment device based on image analysis includes: a user spinal scanning module 201, which is used to obtain a spinal image sequence of a user;

[0109] The spine feature extraction module 202 is used to use the pre-trained spine function recognition model to perform spine key point recognition on the spine image sequence to obtain spine key point information, and perform feature engineering feature calculation on the spine key point information to obtain a spine state feature set;

[0110] The spinal function assessment module 203 is used to perform a fully connected feature recognition operation on the spinal state feature set based on a preset target spinal function type, obtain the scores of each key factor corresponding to the target spinal function type, and perform weighted sum calculation on each score based on the preset assessment weights of the target spinal function type for each key factor, so as to obtain the user's assessment score for the target spinal function type.

[0111] It is not difficult to find that this embodiment is a device embodiment corresponding to the above method embodiment, and this embodiment can be implemented in conjunction with the above method embodiment. The relevant technical details mentioned in the above method embodiment are still valid in this embodiment. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above method embodiment.

[0112] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this embodiment, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this embodiment, but this does not mean that there are no other units in this embodiment.

[0113] Another embodiment of the present invention relates to an electronic device, such as Figure 3 As shown, it includes at least one processor 301; and a memory 302 that is communicatively connected to the at least one processor; wherein the memory 302 stores instructions that can be executed by the at least one processor 301, and the instructions are executed by the at least one processor 301 so that the at least one processor 301 can perform the spinal function assessment method based on image analysis as described above.

[0114] The memory 302 and the processor 301 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 301 and the memory 302 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 301 is transmitted on a wireless medium through an antenna, and further, the antenna also receives data and transmits the data to the processor 301.

[0115] The processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 302 can be used to store data used by the processor 301 when performing operations.

[0116] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0117] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0118] Those skilled in the art will appreciate that the above-mentioned embodiments are specific examples for implementing the present embodiment, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present embodiment.

Claims

1. A spinal function assessment method based on image analysis, characterized in that: The method comprises: Obtaining a spine image sequence of a user; Using a pre-trained spinal function recognition model, the spinal key point recognition is performed on the spinal image sequence to obtain spinal key point information, and feature engineering feature calculation is performed on the spinal key point information to obtain a spinal state feature set; A fully connected feature recognition operation based on a preset target spinal function type is performed on the spinal state feature set to obtain scores for each key factor corresponding to the target spinal function type, and a weighted sum calculation is performed on each score based on the preset evaluation weights of the target spinal function type for each key factor to obtain an evaluation score of the user for the target spinal function type.

2. The spinal function assessment method based on image analysis according to claim 1, characterized in that: The step of obtaining a spine image sequence of a user comprises: Acquire a sequence of motion images when the user performs a preset standard detection action; The motion image sequence is subjected to spinal morphology prediction and recognition to obtain spinal prediction results corresponding to each extreme position action image in the motion image sequence, and each spinal prediction result is arranged to obtain a spinal image sequence.

3. The spinal function assessment method based on image analysis according to claim 2, characterized in that: The step of performing spine morphology prediction and recognition on the motion image sequence to obtain spine prediction results corresponding to each extreme position action image in the motion image sequence includes: Performing a feature extraction operation based on human outline and human posture on the motion image sequence to obtain human outline features and human posture features; A spine posture prediction operation is performed on the human body contour features and human body posture features to obtain a spine prediction result sequence corresponding to the motion image sequence, and spine prediction results corresponding to each extreme position action image in the motion image sequence are extracted from the spine prediction result sequence.

4. The spinal function assessment method based on image analysis according to claim 2, characterized in that: After obtaining the spine prediction results corresponding to each extreme position action image in the motion image sequence, the method comprises: Performing an image denoising operation on the spine prediction result using a Gaussian algorithm to obtain a denoised image, and performing a contrast enhancement operation on the denoised image using a histogram equalization algorithm to obtain a contrast enhanced image; The contrast enhanced image is subjected to spine detection and interception operations using an edge detection algorithm to obtain an image enhanced spine prediction result.

5. The spinal function assessment method based on image analysis according to claim 1, characterized in that: Before using the pre-trained spinal function recognition model, the method further includes: Obtain a pre-built spinal function recognition model and a training sample set based on clinical statistics; Extracting a target training sample from the training sample set in turn, performing feature extraction operation on the target training sample using the spinal function recognition model, and performing random forest classification judgment on the feature extraction result to obtain a predicted spinal assessment result; According to the random forest algorithm, the relevance score of each feature in the feature extraction result to the predicted spinal assessment result is obtained, and according to the arrangement result of each relevance score, the key features in the feature extraction result are obtained; Calculating the loss value between the predicted spine assessment result and the true label of the target training sample using a cross entropy loss algorithm; Calculating the minimization of the loss value to obtain model parameters, and performing a network back-propagation operation on the spinal function recognition model according to the model parameters to obtain an updated spinal function recognition model; Determining the convergence of the loss value; When the loss value does not converge, return to the above step of extracting a target training sample from the training sample set in sequence, and train the spinal function recognition model for spinal status assessment based on the key features; When the loss value converges, the training process is stopped to obtain a trained spinal function recognition model.

6. The spinal function assessment method based on image analysis according to claim 1, characterized in that: The feature engineering feature calculation is performed on the spine key point information to obtain a spine state feature set, including: Acquire key features in the spinal function recognition model, and perform secondary construction on each of the key features based on preset feature engineering to obtain secondary features; According to the weight configuration rules in the preset feature engineering, the key features and the secondary features are weighted to obtain a spinal state feature set.

7. The method for evaluating spinal function based on image analysis according to claim 1, characterized in that: After acquiring the user's spine image sequence, the method further includes: Obtain the user's default lying posture and real spine image; Performing spinal feature recognition on the real spinal image to obtain a real spinal feature set; Extracting a target spinal image from the spinal image sequence in sequence, and acquiring a target motion image corresponding to the target spinal image; Identifying motion change features of the target motion image and the default lying down motion; According to the real spine feature set and the motion change feature, the target spine image is subjected to spine morphology correction to obtain a corrected spine image corresponding to the target motion image; The corrected spinal images corresponding to the target spinal images in the spinal image sequence are acquired to obtain a spinal image sequence personalized and corrected by the user.

8. A spinal function assessment device based on image analysis, characterized in that: The device comprises: A user spine scanning module, used to obtain a user's spine image sequence; A spine feature extraction module is used to use a pre-trained spine function recognition model to perform spine key point recognition on the spine image sequence to obtain spine key point information, and perform feature engineering feature calculation on the spine key point information to obtain a spine state feature set; A spinal function assessment module is used to perform a fully connected feature recognition operation on the spinal status feature set based on a preset target spinal function type, obtain the score of each key factor corresponding to the target spinal function type, and perform a weighted sum calculation on each score based on the preset assessment weights of the target spinal function type for each key factor, so as to obtain the user's assessment score for the target spinal function type.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the spinal function assessment method based on image analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the spinal function assessment method based on image analysis according to any one of claims 1 to 7 is implemented.

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