Anesthesia site identification method and system based on image navigation technology

Abstract

The present invention relates to the field of anesthesia site identification technology, and is a method and system for anesthesia site identification based on image navigation technology, comprising: performing a digitization operation on an original spinal image to obtain a standard spinal image, performing a comparison operation on the standard spinal image using a pre-built spinal image database to obtain multiple potential anesthesia sites, sending the optimized spinal image to a pre-built spinal management system, receiving a returned spinal image from a spinal diagnosis client, aggregating the returned spinal images to obtain multiple returned spinal images, obtaining multiple calibrated anesthesia sites based on the multiple returned spinal images, obtaining a target anesthesia site based on the multiple calibrated anesthesia sites, and completing the identification of the anesthesia site based on the target anesthesia site. Therefore, the present invention aims to improve the accuracy of anesthesia site identification.

Description

Technical Field

[0001] The present invention relates to the technical field of anesthesia site recognition, and in particular to an anesthesia site recognition method, system, electronic device and computer-readable storage medium based on image navigation technology. Background Art

[0002] Identification of the anesthesia site plays an important role in surgery. Correct identification of the anesthesia site can improve the anesthesia effect and the safety of anesthetic drugs, and avoid damage to surrounding tissues and nerves during anesthetic injection.

[0003] While anesthesia site identification plays a crucial role during surgery, it often relies on the physician's experience due to the diverse patient conditions. Failure to determine the most appropriate anesthesia site based on the patient's condition can compromise patient safety. Therefore, improving the accuracy of anesthesia site identification is an urgent issue. Summary of the Invention

[0004] The present invention provides an anesthesia site identification method based on image navigation technology, the main purpose of which is to improve the accuracy of anesthesia site identification.

[0005] To achieve the above objectives, the present invention provides an anesthesia site identification method based on image navigation technology, comprising:

[0006] receiving an identification instruction of an anesthesia site, and activating a pre-built imaging device and a pre-built spinal fixation device according to the identification instruction;

[0007] Identifying the spine of the patient to be anesthetized, performing a fixation operation on the patient's spine using a spinal fixation device to obtain a fixed spine, and performing a photographing operation on the fixed spine using an imaging device to obtain an original spinal image;

[0008] Performing digital operations on the original spinal image to obtain a standard spinal image;

[0009] Performing a comparison operation on a standard spinal image using a pre-built spinal image database to obtain a plurality of potential anesthesia sites, wherein the spinal image database includes a plurality of reference spinal images, and a distance between a potential anesthesia site and another potential anesthesia site is at least 0.5 cm;

[0010] A spinal image coordinate system is constructed in a standard spinal image, a plurality of potential anesthesia coordinates are obtained based on a plurality of potential anesthesia sites, and the plurality of potential anesthesia coordinates are projected onto a standard spinal image containing the spinal image coordinate system to obtain an optimized spinal image;

[0011] Sending the optimized spinal image to a pre-built spinal management system, wherein the spinal management system includes a spinal diagnosis client corresponding to each of a plurality of spinal diagnosis experts;

[0012] When the spine management system successfully receives the optimized spine image, the spine management system is used to perform a connection operation on the spine diagnosis client. When the spine management system successfully connects to the spine diagnosis client, the spine management system is used to send the optimized spine image to the spine diagnosis client and receive the returned spine image from the spine diagnosis client.

[0013] Summarizing the returned spinal images to obtain a plurality of returned spinal images, and acquiring a plurality of calibrated anesthesia sites based on the plurality of returned spinal images;

[0014] A target anesthesia site is obtained according to a plurality of calibrated anesthesia sites, and the anesthesia site is identified based on the target anesthesia site.

[0015] Optionally, performing a digitization operation on the original spinal image to obtain a standard spinal image includes:

[0016] Acquiring an initial spinal image based on the original spinal image, performing an upsampling operation on the initial spinal image to obtain an updated spinal image, wherein the updated spinal image includes a plurality of updated pixel points;

[0017] Performing a first quantization operation on the updated spine image using a preset initial quantization bit number to obtain a plurality of spine pixel points;

[0018] Acquire a grayscale value set using a plurality of spine pixel points, wherein the grayscale value set includes a plurality of grayscale values, and the grayscale values ​​correspond one-to-one to the spine pixel points;

[0019] Obtaining a grayscale value variance based on the grayscale value set, wherein the grayscale value variance is a variance of multiple grayscale values ​​in the grayscale value set;

[0020] Comparing the grayscale value variance with a preset grayscale variance threshold;

[0021] If the grayscale value variance is less than the grayscale variance threshold, a quantization update operation is performed on the initial quantization bit number using multiple spine pixel points as multiple evaluation pixel points to obtain an updated quantization bit number;

[0022] Performing a second quantization operation on the plurality of evaluation pixels using the updated quantization bit number to obtain a plurality of target pixels, and performing an encoding operation on the plurality of target pixels to obtain a plurality of encoded pixels;

[0023] A coded spinal image is obtained according to a plurality of coded pixel points, a coded definition is obtained based on the coded spinal image, and the coded spinal image is optimized using the coded definition to obtain a standard spinal image.

[0024] Optionally, optimizing the encoded spinal image using encoding clarity to obtain a standard spinal image includes:

[0025] Compare the encoding clarity with the preset spine clarity threshold;

[0026] If the coding clarity is greater than or equal to the spine clarity threshold, the coded spine image is used as the standard spine image;

[0027] If the encoding clarity is less than the spine clarity threshold, a denoising operation is performed on the encoded spine image to obtain a denoised spine image, and a sharpening operation is performed on the denoised spine image to obtain a standard spine image.

[0028] Optionally, the pre-built spinal image database is used to perform a comparison operation on the standard spinal image to obtain multiple potential anesthesia sites, including:

[0029] Performing information extraction operations on standard spinal images to obtain standard patient information;

[0030] The following operations are performed on each of the multiple reference spine images:

[0031] Acquiring reference patient information based on the reference spinal images, wherein the number of the reference spinal images is the same as the number of the reference patient information, and the dimensions of the standard patient information are the same as the dimensions of the reference patient information, wherein the dimensions of the standard patient information and the dimensions of the reference patient information both include a gender dimension, a weight dimension, an age dimension, a height dimension, and a spinal posture dimension;

[0032] A reference spine sequence is constructed based on the reference patient information, where the reference spine sequence is as follows:

[0033] J i =[A, B, C, D, E]

[0034] Among them, J i represents the reference spine sequence corresponding to the i-th reference spine image in multiple reference spine images, A represents the gender dimension, B represents the weight dimension, C represents the age dimension, D represents the height dimension, and E represents the spine posture dimension;

[0035] Summarizing the reference vertebral sequences to obtain multiple reference vertebral sequences;

[0036] A standard spinal sequence is constructed using standard patient information, a plurality of target spinal sequences are identified from a plurality of reference spinal sequences using the standard spinal sequence, and a plurality of first spinal images are extracted from a spinal image database using the plurality of target spinal sequences, wherein the target spinal sequences correspond one-to-one to the first spinal images;

[0037] Multiple potential anesthesia sites are identified using multiple first spinal images.

[0038] Optionally, the identifying of multiple potential anesthesia sites using multiple first spinal images includes:

[0039] A standard spine contour is obtained according to a standard spine image, and the following operation is performed on each of the multiple first spine images:

[0040] obtaining a first spinal contour according to the first spinal image, constructing a spinal similarity formula, and calculating a spinal similarity value using the spinal similarity formula, the first spinal contour, and a standard spinal contour to obtain a plurality of spinal similarity values;

[0041] Acquiring a target spinal image based on a plurality of spinal similarity values, wherein the target spinal image is a first spinal image corresponding to a maximum spinal similarity value among the plurality of spinal similarity values;

[0042] Multiple potential anesthesia sites were obtained based on the target spinal image.

[0043] Optionally, constructing a spine similarity formula includes:

[0044] Performing a vertebral body recognition operation on the standard vertebral contour to obtain a plurality of standard vertebral bodies, and identifying a first vertebral body and a second vertebral body from the plurality of standard vertebral bodies;

[0045] Identify the upper edge of the first vertebra and the lower edge of the second vertebra respectively, and draw tangent lines at the upper edge of the first vertebra and the lower edge of the second vertebra to obtain the first vertebra tangent line and the second vertebra tangent line;

[0046] Performing a perpendicular line drawing operation on the first vertebral body tangent and the second vertebral body tangent to obtain a first vertebral body perpendicular line and a second vertebral body perpendicular line, and performing a perpendicular line extension operation on the first vertebral body perpendicular line and the second vertebral body perpendicular line to obtain a first extended perpendicular line and a second extended perpendicular line;

[0047] Obtaining a spinal extension angle according to the first extended perpendicular line and the second extended perpendicular line, performing an angle measurement operation on the spinal extension angle to obtain a spinal angle;

[0048] Constructing a standard spine matrix based on the weight dimension, age dimension, height dimension and spine intersection angle;

[0049] Acquire a first intersection angle based on the first spinal image, and summarize the first intersection angles to obtain a plurality of first intersection angles;

[0050] The following operation is performed on each of the multiple first intersection angles:

[0051] Constructing a first spine matrix using the first intersection angle, weight dimension, age dimension, and height dimension;

[0052] The spine similarity formula is constructed using the standard spine matrix and the first spine matrix.

[0053] Optionally, constructing a spine similarity formula using the standard spine matrix and the first spine matrix includes:

[0054] Among them, the standard spine matrix is ​​shown as follows:

[0055]

[0056] Among them, Z0 represents the standard spine matrix, α represents the spine intersection angle, β represents the weight dimension, γ represents the age dimension, and δ represents the height dimension;

[0057] The spine similarity formula is constructed using the standard spine matrix, the first spine matrix and the preset extreme value coefficient, where the spine similarity formula is as follows:

[0058]

[0059] Among them, S represents the spine similarity formula, Z i represents the first spine matrix corresponding to the i-th first spine image in multiple first spine images, a represents the extreme value coefficient, || || F represents the Frobenius norm.

[0060] Optionally, projecting the plurality of potential anesthesia coordinates onto a standard spinal image containing a spinal image coordinate system to obtain an optimized spinal image comprises:

[0061] Constructing a target spinal coordinate system using a target spinal image, and confirming the potential anesthesia coordinates in the target spinal coordinate system using a plurality of potential anesthesia coordinates to obtain a plurality of potential spinal coordinates, wherein the potential spinal coordinates correspond one-to-one to the potential anesthesia coordinates;

[0062] performing a coordinate extraction operation on each of the plurality of potential spine coordinates to obtain a plurality of extracted spine coordinates;

[0063] Annotating the multiple extracted vertebral coordinates to the vertebral image coordinate system to obtain multiple annotated vertebral coordinates;

[0064] Performing a labeling operation on each of the plurality of labeled spinal coordinates to obtain a plurality of labeled spinal coordinates, wherein the potential spinal coordinates correspond one-to-one to the labeled spinal coordinates;

[0065] The standard spine image containing multiple labeled spine coordinates is recorded as the optimized spine image.

[0066] Optionally, obtaining a target anesthesia site according to a plurality of calibrated anesthesia sites includes:

[0067] Get the number of online clients of the spine diagnosis client;

[0068] If the number of online clients is greater than or equal to a preset minimum threshold and less than or equal to a preset maximum threshold, repetition rates are calculated for multiple calibrated anesthesia sites to obtain one or more repetition rates;

[0069] Acquire a target anesthesia site based on the one or more repetition rates, wherein the target anesthesia site is a calibrated anesthesia site corresponding to a maximum repetition rate among the one or more repetition rates;

[0070] If the number of online clients is greater than the maximum threshold, the calibrated anesthesia sites corresponding to the returned spinal images are sorted in descending order according to the time corresponding to the returned spinal images to obtain a calibrated anesthesia site sequence;

[0071] Based on the maximum threshold, an initial anesthesia site sequence is selected from the calibrated anesthesia site sequence, wherein the initial anesthesia site sequence includes multiple initial anesthesia sites, and the number corresponding to the initial anesthesia sites is equal to the maximum threshold, and the target anesthesia site is obtained based on the initial anesthesia site sequence.

[0072] To achieve the above objectives, the present invention further provides an anesthesia site identification system based on image navigation technology, comprising:

[0073] An instruction receiving module is used to receive an identification instruction of an anesthesia site and start a pre-built imaging device and a pre-built spinal fixation device according to the identification instruction;

[0074] Identifying the spine of the patient to be anesthetized, performing a fixation operation on the patient's spine using a spinal fixation device to obtain a fixed spine, and performing a photographing operation on the fixed spine using an imaging device to obtain an original spinal image;

[0075] A coordinate construction module is used to perform digital operations on the original spinal image to obtain a standard spinal image;

[0076] Performing a comparison operation on a standard spinal image using a pre-built spinal image database to obtain a plurality of potential anesthesia sites, wherein the spinal image database includes a plurality of reference spinal images, and a distance between a potential anesthesia site and another potential anesthesia site is at least 0.5 cm;

[0077] a system management module, configured to construct a spinal image coordinate system in a standard spinal image, obtain a plurality of potential anesthesia coordinates based on a plurality of potential anesthesia sites, and project the plurality of potential anesthesia coordinates into the standard spinal image containing the spinal image coordinate system to obtain an optimized spinal image;

[0078] Sending the optimized spinal image to a pre-built spinal management system, wherein the spinal management system includes a spinal diagnosis client corresponding to each of a plurality of spinal diagnosis experts;

[0079] A site determination module is configured to, when the spine management system successfully receives the optimized spine image, use the spine management system to perform a connection operation on the spine diagnosis client; when the spine management system successfully connects to the spine diagnosis client, use the spine management system to send the optimized spine image to the spine diagnosis client, and receive the returned spine image from the spine diagnosis client;

[0080] Summarizing the returned spinal images to obtain a plurality of returned spinal images, and acquiring a plurality of calibrated anesthesia sites based on the plurality of returned spinal images;

[0081] A target anesthesia site is obtained according to a plurality of calibrated anesthesia sites, and the anesthesia site is identified based on the target anesthesia site.

[0082] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0083] a memory storing at least one instruction; and

[0084] The processor executes the instructions stored in the memory to implement the above-mentioned anesthesia site identification method based on image navigation technology.

[0085] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned anesthesia site identification method based on image navigation technology.

[0086] The present invention is to solve the problems described in the background technology. An embodiment of the present invention identifies the spine of a patient to be anesthetized, and uses a spinal fixation device to perform a fixation operation on the patient's spine to obtain a fixed spine. It can be seen that the present invention takes into account the problem that different patient postures may cause inaccurate detection of the patient's spine. Furthermore, by fixing the patient's spine, the accuracy of detection of the patient's spine can be improved. A digitization operation is performed on the original spinal image to obtain a standard spinal image. It can be seen that the digitization of the original spinal image by the embodiment of the present invention is beneficial to the storage and transmission of the original spinal image. A comparison operation is performed on the standard spinal image using a pre-built spinal image database to obtain multiple potential anesthesia sites. It can be seen that the embodiment of the present invention uses historical data to obtain a reference spinal image that is highly similar to the standard spinal image, and uses historical experience to quickly and accurately find the anesthesia site, constructs a spinal image coordinate system in the standard spinal image, obtains multiple potential anesthesia coordinates based on multiple potential anesthesia sites, and projects the multiple potential anesthesia coordinates onto the standard spinal image containing the spinal image coordinate system. In the image, an optimized spinal image is obtained. It can be seen that the present invention determines the potential anesthesia site with the help of a coordinate system, ensures the correctness of the potential anesthesia site, and sends the optimized spinal image to a pre-built spinal management system. The spinal management system in this embodiment is constructed to better manage spinal image data, and can improve the accuracy and timeliness of anesthesia sites when searching for anesthesia sites. The returned spinal images from the spinal diagnosis client are received, and the returned spinal images are summarized to obtain multiple returned spinal images. Based on the multiple returned spinal images, multiple calibrated anesthesia sites are obtained, and the target anesthesia site is obtained based on the multiple calibrated anesthesia sites. The anesthesia site is identified based on the target anesthesia site. It can be seen that in the embodiment of the present invention, each spinal diagnosis client corresponds to a spinal diagnosis expert with professional knowledge. The anesthesia site selected by each spinal diagnosis expert based on the optimized spinal image and the target anesthesia site is confirmed based on the anesthesia site selected by the spinal diagnosis expert. The confirmed target anesthesia site improves the accuracy of anesthesia site confirmation for patients. Therefore, the main purpose of the present invention is to improve the accuracy of anesthesia site identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 A schematic flow chart of an anesthesia site identification method based on image navigation technology provided in one embodiment of the present invention;

[0088] Figure 2 An original spinal image for the anesthesia site identification method based on image navigation technology provided in one embodiment of the present invention;

[0089] Figure 3 A reference spinal image for the anesthesia site identification method based on image navigation technology provided in one embodiment of the present invention;

[0090] Figure 4A functional module diagram of an anesthesia site recognition system based on image navigation technology provided by one embodiment of the present invention;

[0091] Figure 5 A schematic structural diagram of an electronic device for implementing the anesthesia site identification method based on image navigation technology provided in one embodiment of the present invention.

[0092] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0093] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0094] The embodiments of the present application provide a method for identifying anesthesia sites based on image navigation technology. The execution subject of the method for identifying anesthesia sites based on image navigation technology includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for identifying anesthesia sites based on image navigation technology can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0095] Reference Figure 1 FIG. 1 is a flow chart of an anesthesia site identification method based on image navigation technology according to an embodiment of the present invention. In this embodiment, the anesthesia site identification method based on image navigation technology includes:

[0096] S1. Receive an identification instruction of an anesthesia site, and start a pre-built imaging device and a pre-built spinal fixation device according to the identification instruction.

[0097] It should be noted that the anesthesia site refers to the injection point for injecting anesthetic drugs, such as the arm or spine, which can serve as an anesthesia site. In the embodiment of the present invention, the anesthesia site is the patient's spine. In addition, the anesthesia site identification instruction is generally initiated by the operator performing anesthesia on the patient's spine.

[0098] Furthermore, an imaging device refers to a device that forms a medical image based on the anesthesia site. In embodiments of the present invention, the imaging device is a device that uses the magnetic field and radiofrequency pulses of magnetic resonance imaging to image the patient's spine. Optionally, a nuclear magnetic resonance device is used as the imaging device. For example, if Xiao Zhang requires anesthesia, the imaging device is first used to image Xiao Zhang's spine, thereby generating an image of the spine. The anesthesia site is then determined based on the spinal image.

[0099] In addition, the spinal fixation device refers to a device that fixes the patient's spine. In this embodiment, a restraint belt is used to fix the patient's spine to prevent inaccurate spinal image generation due to different patient postures.

[0100] S2. Identify the spine of the patient to be anesthetized, use a spinal fixation device to fix the patient's spine to obtain a fixed spine, and use an imaging device to photograph the fixed spine to obtain an original spinal image.

[0101] It should be explained that the patient spine refers to the spine of the patient to be examined. In order to ensure the accuracy and clarity of the generated spinal image, it is necessary to perform a fixation operation on the patient's spine. The fixation operation refers to the operation of fixing the patient's posture. For example, when it is necessary to photograph Xiao Zhang's spine, Xiao Zhang's posture can be fixed with a restraint belt, and then the nuclear magnetic resonance device can be used to photograph Xiao Zhang's spine to obtain the original spinal image. At this time, Xiao Zhang's spine in the fixed posture is the fixed spine.

[0102] The key point is to start the magnetic field and radio frequency pulse in the imaging device, use the magnetic field and radio frequency pulse to fix the spine, and obtain the original spinal image. Figure 2 Shown is the original spinal image.

[0103] S3. Perform a digitization operation on the original spinal image to obtain a standard spinal image.

[0104] It should be emphasized that the imaging effect of the original spinal image may be poor, so performing digital operations on the original spinal image can improve the imaging quality of the original spinal image, thereby improving the accuracy of identifying the anesthesia site.

[0105] Specifically, performing a digitization operation on the original spinal image to obtain a standard spinal image includes:

[0106] Acquiring an initial spinal image based on the original spinal image, performing an upsampling operation on the initial spinal image to obtain an updated spinal image, wherein the updated spinal image includes a plurality of updated pixel points;

[0107] Performing a first quantization operation on the updated spine image using a preset initial quantization bit number to obtain a plurality of spine pixel points;

[0108] Acquire a grayscale value set using a plurality of spine pixel points, wherein the grayscale value set includes a plurality of grayscale values, and the grayscale values ​​correspond one-to-one to the spine pixel points;

[0109] Obtaining a grayscale value variance based on the grayscale value set, wherein the grayscale value variance is a variance of multiple grayscale values ​​in the grayscale value set;

[0110] Comparing the grayscale value variance with a preset grayscale variance threshold;

[0111] If the grayscale value variance is less than the grayscale variance threshold, a quantization update operation is performed on the initial quantization bit number using multiple spine pixel points as multiple evaluation pixel points to obtain an updated quantization bit number;

[0112] Performing a second quantization operation on the plurality of evaluation pixels using the updated quantization bit number to obtain a plurality of target pixels, and performing an encoding operation on the plurality of target pixels to obtain a plurality of encoded pixels;

[0113] A coded spinal image is obtained according to a plurality of coded pixel points, a coded definition is obtained based on the coded spinal image, and the coded spinal image is optimized using the coded definition to obtain a standard spinal image.

[0114] It should be understood that obtaining the initial spinal image based on the original spinal image means extracting the original spinal image into the initial spinal image in the form of pixels, where each pixel corresponds to a grayscale value. The upsampling technique is conventional and will not be described in detail here. The purpose of performing the upsampling operation on the initial spinal image is to improve the clarity of the initial spinal image and, thereby, improve the accuracy of anesthesia site identification. The initial quantization bit number refers to the number of grayscale levels. For example, when using 16 grayscale levels, the initial quantization bit number is 16. The first quantization operation refers to the operation of converting the grayscale value corresponding to each of the multiple updated pixels to the initial quantization bit number, that is, converting the color corresponding to each of the multiple pixels corresponding to the initial spinal image into a more refined color. For example, if the grayscale values ​​of the pixels in the initial spinal image use 8 grayscale levels and the initial quantization bit number is 16, the first quantization operation can convert the grayscale value corresponding to the 8 grayscale levels in the initial spinal image into the grayscale value corresponding to the 16 grayscale levels, thereby improving the accuracy of the color representation of the updated spinal image.

[0115] It is understood that performing the first quantization operation on the updated spinal image using the initial quantization bit number is an operation of grayscale conversion of each pixel in the updated spinal image using the grayscale level corresponding to the initial quantization bit number. For example, when a 16-level grayscale level is used, the grayscale value corresponding to the pixel representing white in the updated spinal image is 16. The technology of using grayscale levels to obtain the grayscale value corresponding to the pixel is existing in the art and will not be further described here.

[0116] Furthermore, the initial sampling frequency is preset because different sampling frequencies significantly affect the resolution and clarity of the initial spinal image. If the initial spinal image obtained using the initial sampling frequency fails to meet the required resolution and clarity, a frequency update operation is performed on the initial sampling frequency, adjusting the initial sampling frequency to a higher updated sampling frequency. Re-sampling is then performed using the updated sampling frequency to obtain an updated spinal image with higher resolution and clarity.

[0117] Understandably, after the first quantization operation, multiple spinal pixels may still not be able to represent the patient's spinal condition. Therefore, a second quantization operation is performed on multiple evaluation pixels using an updated quantization bit count to obtain multiple target pixels. This can improve the color refinement of the multiple target pixels. The higher the number of grayscale levels corresponding to the initial quantization bit count, the more colors can be represented, and the updated quantization bit count is higher than the initial quantization bit count.

[0118] It should be understood that when the grayscale value variance is less than the grayscale variance threshold, it indicates that the difference between multiple spinal pixel points is not obvious enough or cannot represent the condition of the patient's spine. Therefore, it is necessary to obtain multiple target pixel points with more obvious features through multiple evaluation pixel points.

[0119] Key points: the encoding operation converts the quantized target pixels into a medical digital imaging and communication format for storage and transmission. The medical digital imaging and communication format refers to the format used to store medical images. Storing the encoded pixels in the medical digital imaging and communication format yields an encoded spinal image. In this embodiment, to prevent distortion and unclear spinal images from affecting examination results, a second quantization operation is performed on the spinal pixels using an updated quantization bit count to obtain undistorted or high-definition target pixels.

[0120] It is understood that the purpose of the coded spinal image is to examine Xiao Zhang's spine and obtain relevant spinal information. To ensure the accuracy of the examination results, the clarity of the coded spinal image must be guaranteed. A clarity calculation is performed on the coded spinal image. A coded spinal image that meets the required clarity is considered a clear spinal image, which is the standard spinal image. The coded clarity refers to the signal-to-noise ratio of the coded spinal image. The technology for obtaining coded clarity using coded spinal images is existing technology and will not be elaborated on here.

[0121] Furthermore, the method of optimizing the encoded spinal image using encoding clarity to obtain a standard spinal image includes:

[0122] Compare the encoding clarity with the preset spine clarity threshold;

[0123] If the coding clarity is greater than or equal to the spine clarity threshold, the coded spine image is used as the standard spine image;

[0124] If the encoding clarity is less than the spine clarity threshold, a denoising operation is performed on the encoded spine image to obtain a denoised spine image, and a sharpening operation is performed on the denoised spine image to obtain a standard spine image.

[0125] It should be understood that the role of encoding clarity is to judge the clarity of the encoded spinal image. When the original spinal image is collected, noise that affects the quality and clarity of the original spinal image will be generated. The denoising operation is to ensure the quality and clarity of the original spinal image. Optionally, a Gaussian filter is used to perform a denoising operation on the encoded spinal image. The spine clarity threshold is the minimum clarity that the encoded spinal image is expected to have. The sharpening operation refers to image sharpening, and the sharpening operation is intended to enhance the edges and details of the denoised spinal image, making the denoised spinal image look clearer and more vivid. Optionally, the Laplace operator is used to sharpen the denoised spinal image to obtain a standard spinal image.

[0126] For example, if the encoding clarity is 30dB, the spine clarity threshold is 35dB. When comparing 30dB and 35dB, if 30dB is less than 35dB, it means that the encoding clarity does not meet the required clarity. In this case, denoising and sharpening operations need to be performed on the encoded spine image to obtain a standard spine image. If the encoding clarity is 36dB, the encoded spine image is used as the standard spine image.

[0127] S4. Use a pre-built spinal image database to perform a comparison operation on the standard spinal image to obtain multiple potential anesthesia sites, wherein the spinal image database includes multiple reference spinal images, and the distance between the potential anesthesia site and another potential anesthesia site is at least 0.5 cm.

[0128] It should be understood that the spinal image database includes multiple reference spinal images, and multiple reference anesthesia sites have been marked in each reference spinal image. The distance between potential anesthesia sites is limited in order to avoid obtaining countless potential anesthesia sites. For example, Figure 3 Shown is one of the reference spine images. Figure 3 In the reference spine image, the gray circle is the reference anesthesia site.

[0129] Furthermore, the pre-built spinal image database is used to perform a comparison operation on the standard spinal image to obtain multiple potential anesthesia sites, including:

[0130] Performing information extraction operations on standard spinal images to obtain standard patient information;

[0131] The following operations are performed on each of the multiple reference spine images:

[0132] Acquiring reference patient information based on the reference spinal images, wherein the number of the reference spinal images is the same as the number of the reference patient information, and the dimensions of the standard patient information are the same as the dimensions of the reference patient information, wherein the dimensions of the standard patient information and the dimensions of the reference patient information both include a gender dimension, a weight dimension, an age dimension, a height dimension, and a spinal posture dimension;

[0133] A reference spine sequence is constructed based on the reference patient information, where the reference spine sequence is as follows:

[0134] J i =[A, B, C, D, E]

[0135] Among them, J i represents the reference spine sequence corresponding to the i-th reference spine image in multiple reference spine images, A represents the gender dimension, B represents the weight dimension, C represents the age dimension, D represents the height dimension, and E represents the spine posture dimension;

[0136] Summarizing the reference vertebral sequences to obtain multiple reference vertebral sequences;

[0137] A standard spinal sequence is constructed using standard patient information, a plurality of target spinal sequences are identified from a plurality of reference spinal sequences using the standard spinal sequence, and a plurality of first spinal images are extracted from a spinal image database using the plurality of target spinal sequences, wherein the target spinal sequences correspond one-to-one to the first spinal images;

[0138] Multiple potential anesthesia sites are identified using multiple first spinal images.

[0139] It is understandable that the standard patient information is the patient information corresponding to the patient of the standard spinal image, and the definition of the reference patient information is the same as that of the standard patient information. Both the standard patient information and the reference patient information have been recorded when the patient's spine is examined, so the standard spinal image corresponds to the standard patient information, and each reference spinal image corresponds to one patient information. The reference spinal sequence contains the reference patient information. The standard spinal sequence and the reference spinal sequence are constructed in the same way and have the same effect, which will not be repeated here. The gender dimension of the standard patient information is used to screen in the reference spinal sequence to narrow the range of the number of reference spinal images. Each of the remaining multiple reference spinal sequences that meet the requirements is a target spinal sequence, and the reference spinal image corresponding to each target spinal sequence in the multiple target spinal sequences is the first spinal image. The gender dimension refers to gender, the weight dimension refers to weight, the age dimension refers to the patient's age, the height dimension refers to the patient's height, and the spinal posture dimension is the patient's spinal posture. Obviously, after the patient is fixed, the patient's spinal posture dimension is only related to the disease on the patient's spine.

[0140] It should be understood that the purpose of constructing reference spinal sequences and standard spinal sequences is to use the parameter contents corresponding to the reference spinal sequences and standard spinal sequences to perform the first round of screening in the spinal imaging database, narrow the scope of the spinal imaging database, reduce the time required for subsequent searches, and improve search efficiency. Here is the first round of screening of reference spinal images, which reduces the workload of subsequent screening.

[0141] Importantly, the use of multiple first spinal images to identify multiple potential anesthesia sites includes:

[0142] A standard spine contour is obtained according to a standard spine image, and the following operation is performed on each of the multiple first spine images:

[0143] obtaining a first spinal contour according to the first spinal image, constructing a spinal similarity formula, and calculating a spinal similarity value using the spinal similarity formula, the first spinal contour, and a standard spinal contour to obtain a plurality of spinal similarity values;

[0144] Acquiring a target spinal image based on a plurality of spinal similarity values, wherein the target spinal image is a first spinal image corresponding to a maximum spinal similarity value among the plurality of spinal similarity values;

[0145] Multiple potential anesthesia sites were obtained based on the target spinal image.

[0146] Key points: The spinal contour in each first spinal image is extracted using a conventional edge detection algorithm to obtain multiple first spinal contours. The method for obtaining the standard spinal contour is the same as for the multiple first spinal contours and achieves the same effect, so this will not be repeated here. A similarity formula is constructed, and the standard spinal contour and each first spinal contour are calculated to determine whether the standard spinal contour and the first spinal contour are similar. The first spinal contour that is most similar to the standard spinal contour is selected as the target spinal image.

[0147] For example, there are five spinal similarity values, namely 0.6, 0.4, 0.33, 0.91, and 0.9. The maximum spinal similarity value is 0.91, and the first spinal image corresponding to 0.91 is the target spinal image. The multiple anesthesia sites in the target spinal image are used as multiple potential anesthesia sites. The similarity formula constructed here is the final screening, directly screening the target spinal image from the first spinal image, and obtaining the target anesthesia site based on the potential anesthesia sites existing in the target spinal image.

[0148] It is understandable that the spine similarity formula is constructed as follows:

[0149] Performing a vertebral body recognition operation on the standard vertebral contour to obtain a plurality of standard vertebral bodies, and identifying a first vertebral body and a second vertebral body from the plurality of standard vertebral bodies;

[0150] Identify the upper edge of the first vertebra and the lower edge of the second vertebra respectively, and draw tangent lines at the upper edge of the first vertebra and the lower edge of the second vertebra to obtain the first vertebra tangent line and the second vertebra tangent line;

[0151] Performing a perpendicular line drawing operation on the first vertebral body tangent and the second vertebral body tangent to obtain a first vertebral body perpendicular line and a second vertebral body perpendicular line, and performing a perpendicular line extension operation on the first vertebral body perpendicular line and the second vertebral body perpendicular line to obtain a first extended perpendicular line and a second extended perpendicular line;

[0152] Obtaining a spinal extension angle according to the first extended perpendicular line and the second extended perpendicular line, performing an angle measurement operation on the spinal extension angle to obtain a spinal angle;

[0153] Constructing a standard spine matrix based on the weight dimension, age dimension, height dimension and spine intersection angle;

[0154] Acquire a first intersection angle based on the first spinal image, and summarize the first intersection angles to obtain a plurality of first intersection angles;

[0155] The following operation is performed on each of the multiple first intersection angles:

[0156] Constructing a first spine matrix using the first intersection angle, weight dimension, age dimension, and height dimension;

[0157] The spine similarity formula is constructed using the standard spine matrix and the first spine matrix.

[0158] It should be understood that both the standard spinal profile and the first spinal profile are composed of a plurality of spinal vertebrae. Since the inclination of each spinal vertebrae is different, the spinal profile of each spinal vertebrae is also different. In the step of performing the vertebral body recognition operation on the standard spinal profile, the vertebral body recognition operation can be performed using an edge detection algorithm. The first spinal vertebrae and the second spinal vertebrae are the spinal vertebrae with the largest inclination and the second largest inclination, respectively, and the first spinal vertebrae is located above the second spinal vertebrae. The inclination refers to the maximum value of the angle between the upper edge or the lower edge of the spinal vertebra and the horizontal line. The weight dimension, age dimension, height dimension and spinal intersection angle are used as elements in the spinal matrix to construct a spinal matrix with a specification of 2×2. The method for obtaining the first intersection angle is the same as the spinal intersection angle, and the method for constructing the first spinal matrix is ​​also the same as the standard spinal matrix, and can achieve the same effect.

[0159] For example, the first vertebral body with the largest inclination and the second vertebral body with the second largest inclination are found in the standard vertebral contour. The upper edge of the first vertebral body refers to the upper edge of the first vertebral body, and the lower edge of the second vertebral body refers to the lower edge of the second vertebral body. Because the first vertebral body is located above the second vertebral body, tangents can be made at the upper edge of the first vertebral body and the lower edge of the second vertebral body to obtain the first vertebral tangent and the second vertebral tangent. Draw a perpendicular line perpendicular to the first vertebral tangent and the second vertebral tangent respectively to obtain the first vertebral perpendicular line and the second vertebral perpendicular line. Because the first vertebral body and the second vertebral body both have an inclination angle, the first vertebral perpendicular line and the second vertebral perpendicular line are extended and intersected at one point. The angle formed by this intersection point, the first vertebral perpendicular line and the second vertebral perpendicular line is the spinal extension angle, and the spinal angle of the spinal extension angle is measured.

[0160] The key point is that the spine similarity formula constructed by using the standard spine matrix and the first spine matrix includes:

[0161] Among them, the standard spine matrix is ​​shown as follows:

[0162]

[0163] Among them, Z0 represents the standard spine matrix, α represents the spine intersection angle, β represents the weight dimension, γ represents the age dimension, and δ represents the height dimension;

[0164] The spine similarity formula is constructed using the standard spine matrix, the first spine matrix and the preset extreme value coefficient, where the spine similarity formula is as follows:

[0165]

[0166] Among them, S represents the spine similarity formula, Z i represents the first spine matrix corresponding to the i-th first spine image in multiple first spine images, a represents the extreme value coefficient, || || F represents the Frobenius norm.

[0167] It should be explained that the purpose of setting the extreme value coefficient a is to avoid the situation where the denominator is zero, and 0<a<1. The Frobenius norm is a norm specifically used to measure the difference between two matrices. The spine similarity formula calculates the spine similarity value between the standard spine profile and each first spine profile. The spine similarity value is used to determine whether the standard spine profile is similar to each first spine profile. According to the formula, the closer the obtained extreme value similarity is to 1, the more similar the standard spine profile is to the first spine profile, and vice versa.

[0168] S5. Construct a spinal image coordinate system in the standard spinal image, obtain multiple potential anesthesia coordinates based on multiple potential anesthesia sites, and project the multiple potential anesthesia coordinates into the standard spinal image containing the spinal image coordinate system to obtain an optimized spinal image.

[0169] It can be understood that the spinal image coordinate system is a two-dimensional coordinate system constructed in the standard spinal image. Among them, in the standard spinal image, there are 5 lumbar vertebrae, and the order from top to bottom is the first lumbar vertebra, the second lumbar vertebra... and the fifth lumbar vertebra. Optionally, the intersection of the lower edge and the left edge of the fifth lumbar vertebra is used as the origin, the direction from the bottom to the top of the spine is the Y-axis, and the direction perpendicular to the Y-axis is the X-axis. The potential anesthesia coordinates are the coordinates of the potential anesthesia site. Establishing a spinal image coordinate system facilitates the comparison of anesthesia sites, so as to quickly and accurately find the coordinates of the potential anesthesia site. Optimizing the spinal image refers to projecting the potential anesthesia coordinates onto the standard spinal image obtained in the spinal image coordinate system.

[0170] For example, a first spinal image that is highly similar to a standard spinal image has been found in the spinal image database. This first spinal image has potential anesthesia sites labeled 1, 2, and 3. The coordinates of these three potential anesthesia sites are 1(1, 2), 2(2, 2), and 3(3, 3), respectively, where 1 represents the number of the potential anesthesia site and (1, 2) represents the coordinates of potential anesthesia site 1. The coordinates of the three potential anesthesia sites are found in the spinal image coordinate system and marked in the form of circles. The locations of the circles are the locations of the potential anesthesia sites.

[0171] Furthermore, the projecting of the plurality of potential anesthesia coordinates into a standard spinal image containing a spinal image coordinate system to obtain an optimized spinal image includes:

[0172] Constructing a target spinal coordinate system using a target spinal image, and confirming the potential anesthesia coordinates in the target spinal coordinate system using a plurality of potential anesthesia coordinates to obtain a plurality of potential spinal coordinates, wherein the potential spinal coordinates correspond one-to-one to the potential anesthesia coordinates;

[0173] performing a coordinate extraction operation on each of the plurality of potential spine coordinates to obtain a plurality of extracted spine coordinates;

[0174] Annotating the multiple extracted vertebral coordinates to the vertebral image coordinate system to obtain multiple annotated vertebral coordinates;

[0175] Performing a labeling operation on each of the plurality of labeled spinal coordinates to obtain a plurality of labeled spinal coordinates, wherein the potential spinal coordinates correspond one-to-one to the labeled spinal coordinates;

[0176] The standard spine image containing multiple labeled spine coordinates is recorded as the optimized spine image.

[0177] It should be understood that the method for constructing the target spinal coordinate system is the same as that of the spinal image coordinate system, and will not be repeated here. There are multiple potential anesthesia sites in the target spinal coordinate system. The corresponding coordinates, namely the potential spinal coordinates, are found in the target spinal coordinate system. Each potential spinal coordinate is used to find the corresponding position in the spinal image coordinate system and mark them to obtain multiple marked spinal coordinates. In order to distinguish multiple marked spinal coordinates, the marked spinal coordinates are randomly numbered in the order of 1, 2, 3, 4... to obtain multiple marked spinal coordinates. The standard spinal image containing multiple marked spinal coordinates is the optimized spinal image.

[0178] Exemplarily, the target spinal coordinate system has three potential anesthesia sites, and the potential spinal coordinates of the three potential anesthesia sites are confirmed to be (1, 1), (2, 2) and (3, 3), respectively. The coordinates (1, 1), (2, 2) and (3, 3) are found in the spinal image coordinate system, respectively, and marked in the form of points, and the three marked spinal coordinates are numbered, where 1 is (1, 1), 2 is (2, 2) and 3 is (3, 3). The marked spinal coordinates after numbering are the marked spinal coordinates, and the standard spinal images including 1 (1, 1), 2 (2, 2) and 3 (3, 3) are optimized spinal images.

[0179] S6. Send the optimized spinal image to a pre-built spinal management system, wherein the spinal management system includes a spinal diagnosis client corresponding to each of the plurality of spinal diagnosis experts.

[0180] For clarification, the spine management system refers to a system that categorizes and manages optimized spine images and multiple reference spine images. This spine management system can transmit optimized spine images requiring anesthesia site confirmation to a spine diagnosis client. A spine diagnosis expert is a specialist with knowledge of spinal medicine and experience in anesthesia injections. A spine diagnosis client is a client used by spine diagnosis experts to access and understand optimized spine images. Each spine diagnosis expert is associated with only one spine diagnosis client.

[0181] S7. When the spinal management system successfully receives the optimized spinal image, the spinal management system is used to perform a connection operation on the spinal diagnosis client. When the spinal management system successfully connects to the spinal diagnosis client, the spinal management system is used to send the optimized spinal image to the spinal diagnosis client, receive the returned spinal image from the spinal diagnosis client, summarize the returned spinal images, obtain multiple returned spinal images, and obtain multiple calibrated anesthesia sites based on the multiple returned spinal images.

[0182] It can be understood that the marked anesthesia site refers to the anesthesia site selected and marked by a spinal anesthesia expert from multiple potential anesthesia sites.

[0183] For example, the optimized spinal image with potential anesthesia sites numbered 1, 2, and 3 is transmitted through the spinal management system to the spinal diagnosis client corresponding to the spinal anesthesia expert. When the spinal anesthesia expert receives the optimized spinal image, he or she selects No. 2 from the three potential anesthesia sites and marks No. 2. The spinal anesthesia expert transmits the optimized spinal image with the marked potential anesthesia sites back to the spinal management system. At this time, the optimized spinal image is the returned spinal image, and the marked potential anesthesia point No. 2 is the calibrated anesthesia site.

[0184] S8. Obtain a target anesthesia site according to the multiple calibrated anesthesia sites, and complete the identification of the anesthesia site based on the target anesthesia site.

[0185] It should be understood that the target anesthesia site refers to the injection point where the anesthetic drug is ultimately injected.

[0186] The key point is that the target anesthesia site is obtained based on multiple calibrated anesthesia sites, including:

[0187] Get the number of online clients of the spine diagnosis client;

[0188] If the number of online clients is greater than or equal to a preset minimum threshold and less than or equal to a preset maximum threshold, repetition rates are calculated for multiple calibrated anesthesia sites to obtain one or more repetition rates;

[0189] Acquire a target anesthesia site based on the one or more repetition rates, wherein the target anesthesia site is a calibrated anesthesia site corresponding to a maximum repetition rate among the one or more repetition rates;

[0190] If the number of online clients is greater than the maximum threshold, the calibrated anesthesia sites corresponding to the returned spinal images are sorted in descending order according to the time corresponding to the returned spinal images to obtain a calibrated anesthesia site sequence;

[0191] Based on the maximum threshold, an initial anesthesia site sequence is selected from the calibrated anesthesia site sequence, wherein the initial anesthesia site sequence includes multiple initial anesthesia sites, and the number corresponding to the initial anesthesia sites is equal to the maximum threshold, and the target anesthesia site is obtained based on the initial anesthesia site sequence.

[0192] Understandably, the number of online clients refers to the number of spinal diagnostic clients that can successfully connect to the spinal management system. The number filtering threshold is set because each spinal anesthesia specialist only selects one calibrated anesthesia site. To ensure the optimal target anesthesia site, the target anesthesia site cannot be acquired between calibrated anesthesia sites calibrated by fewer than two spinal anesthesia specialists, preventing the selection of a suboptimal target anesthesia site. Two is the minimum threshold in the number filtering threshold. Excessive calibrated anesthesia sites complicates subsequent calculations, thereby reducing the efficiency of the entire anesthesia site identification process. The calibrated anesthesia sites calibrated by the spinal anesthesia specialist are selected based on the maximum threshold. The selection criteria are: based on the maximum threshold and the time it takes to transmit the returned spinal image, the returned spinal image is selected as the returned spinal image for screening the target anesthesia site, where the returned spinal image for screening the target anesthesia site includes the calibrated anesthesia site. The calibrated anesthesia site with the highest repetition rate is selected as the target anesthesia site from multiple repetition rates, where the repetition rate is calculated based on the number of times each calibrated anesthesia site is selected from the multiple calibrated anesthesia sites.

[0193] The key point is that the minimum threshold is 2 and the maximum threshold is 10. When the number of returned spinal images exceeds 10, the times of the returned spinal images are 10:00, 10:01, 10:03, 10:04, 10:05, 10:07, 10:09, 10:10, 10:11, 10:16, 10:17, 10:18 and 10:20 respectively. The calibrated anesthesia sites in the times and the corresponding returned spinal images are used to construct a calibrated anesthesia site sequence. The first 10 calibrated anesthesia sites corresponding to the first 10 times in the calibrated anesthesia site sequence are selected in chronological order as the initial anesthesia site sequence, that is, the calibrated anesthesia sites corresponding to 10:00, 10:01, 10:03, 10:04, 10:05, 10:07, 10:09, 10:10, 10:11 and 10:16. Finally, the target anesthesia site is confirmed using the same steps as when the number of online clients is greater than or equal to the minimum threshold and less than or equal to the maximum threshold.

[0194] For example, five spinal anesthesia specialists send back five spinal images, each of which includes a calibrated anesthesia site. For a total of five calibrated anesthesia sites, the repetition rates of the five calibrated anesthesia sites are calculated: site 1 is 10%, site 2 is 60%, site 3 is 10%, site 4 is 12%, and site 5 is 8%. Site 2, with the highest repetition rate, is selected as the target anesthesia site. When the number of calibrated anesthesia sites exceeds a maximum threshold, where the maximum threshold is 10, the first 10 sites are selected as calibrated anesthesia sites based on the maximum threshold value of 10. The subsequent process is the same as when the number of calibrated anesthesia sites is between the minimum and maximum thresholds, and the same effect is achieved.

[0195] The present invention is to solve the problems described in the background technology. An embodiment of the present invention identifies the spine of a patient to be anesthetized, and uses a spinal fixation device to perform a fixation operation on the patient's spine to obtain a fixed spine. It can be seen that the present invention takes into account the problem that different patient postures may cause inaccurate detection of the patient's spine. Furthermore, by fixing the patient's spine, the accuracy of detection of the patient's spine can be improved. A digitization operation is performed on the original spinal image to obtain a standard spinal image. It can be seen that the digitization of the original spinal image by the embodiment of the present invention is beneficial to the storage and transmission of the original spinal image. A comparison operation is performed on the standard spinal image using a pre-built spinal image database to obtain multiple potential anesthesia sites. It can be seen that the embodiment of the present invention uses historical data to obtain a reference spinal image that is highly similar to the standard spinal image, and uses historical experience to quickly and accurately find the anesthesia site, constructs a spinal image coordinate system in the standard spinal image, obtains multiple potential anesthesia coordinates based on multiple potential anesthesia sites, and projects the multiple potential anesthesia coordinates onto the standard spinal image containing the spinal image coordinate system. In the image, an optimized spinal image is obtained. It can be seen that the present invention determines the potential anesthesia site with the help of a coordinate system, ensures the correctness of the potential anesthesia site, and sends the optimized spinal image to a pre-built spinal management system. The spinal management system in this embodiment is constructed to better manage spinal image data, and can improve the accuracy and timeliness of anesthesia sites when searching for anesthesia sites. The returned spinal images from the spinal diagnosis client are received, and the returned spinal images are summarized to obtain multiple returned spinal images. Based on the multiple returned spinal images, multiple calibrated anesthesia sites are obtained, and the target anesthesia site is obtained based on the multiple calibrated anesthesia sites. The anesthesia site is identified based on the target anesthesia site. It can be seen that in the embodiment of the present invention, each spinal diagnosis client corresponds to a spinal diagnosis expert with professional knowledge. The anesthesia site selected by each spinal diagnosis expert based on the optimized spinal image and the target anesthesia site is confirmed based on the anesthesia site selected by the spinal diagnosis expert. The confirmed target anesthesia site improves the accuracy of anesthesia site confirmation for patients. Therefore, the main purpose of the present invention is to improve the accuracy of anesthesia site identification.

[0196] like Figure 4 FIG. 1 is a functional module diagram of an anesthesia site recognition system based on image navigation technology provided by an embodiment of the present invention.

[0197] The image-guided anesthesia site identification system 100 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the image-guided anesthesia site identification system 100 can include an instruction receiving module 101, a coordinate construction module 102, a system management module 103, and a site determination module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These are stored in the electronic device's memory.

[0198] The instruction receiving module 101 is used to receive an identification instruction of an anesthesia site and start a pre-built imaging device and a pre-built spinal fixation device according to the identification instruction;

[0199] Identifying the spine of the patient to be anesthetized, performing a fixation operation on the patient's spine using a spinal fixation device to obtain a fixed spine, and performing a photographing operation on the fixed spine using an imaging device to obtain an original spinal image;

[0200] The coordinate construction module 102 is used to perform a digitization operation on the original spinal image to obtain a standard spinal image;

[0201] Performing a comparison operation on a standard spinal image using a pre-built spinal image database to obtain a plurality of potential anesthesia sites, wherein the spinal image database includes a plurality of reference spinal images, and a distance between a potential anesthesia site and another potential anesthesia site is at least 0.5 cm;

[0202] The system management module 103 is configured to construct a spinal image coordinate system in the standard spinal image, obtain a plurality of potential anesthesia coordinates based on a plurality of potential anesthesia sites, and project the plurality of potential anesthesia coordinates into the standard spinal image containing the spinal image coordinate system to obtain an optimized spinal image;

[0203] Sending the optimized spinal image to a pre-built spinal management system, wherein the spinal management system includes a spinal diagnosis client corresponding to each of a plurality of spinal diagnosis experts;

[0204] The location determination module 104 is configured to, when the spine management system successfully receives the optimized spine image, use the spine management system to perform a connection operation on the spine diagnosis client; when the spine management system successfully connects to the spine diagnosis client, use the spine management system to send the optimized spine image to the spine diagnosis client, and receive the returned spine image from the spine diagnosis client;

[0205] Summarizing the returned spinal images to obtain a plurality of returned spinal images, and acquiring a plurality of calibrated anesthesia sites based on the plurality of returned spinal images;

[0206] A target anesthesia site is obtained according to a plurality of calibrated anesthesia sites, and the anesthesia site is identified based on the target anesthesia site.

[0207] In detail, the modules in the anesthesia site identification system 100 based on image navigation technology in the embodiment of the present invention are used in the same manner as above. Figure 1 The anesthesia site identification method based on image navigation technology described in the invention is the same technical means and can produce the same technical effects, so it will not be repeated here.

[0208] like Figure 5 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing an anesthesia site identification method based on image navigation technology according to an embodiment of the present invention.

[0209] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an anesthesia site identification method program based on image navigation technology.

[0210] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Furthermore, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed on the electronic device 1, such as the code of the anesthesia site recognition method program based on image navigation technology, but can also be used to temporarily store data that has been output or is to be output.

[0211] In some embodiments, the processor 10 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes or runs programs or modules stored in the memory 11 (such as an anesthesia site identification method program based on image navigation technology) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0212] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0213] Figure 5 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 5 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0214] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management system, thereby implementing functions such as charge management, discharge management, and power consumption management through the power management system. The power source may further include any components such as one or more DC or AC power sources, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may further include a variety of sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be described in detail here.

[0215] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0216] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0217] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0218] The anesthesia site identification method program based on image navigation technology stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following:

[0219] receiving an identification instruction of an anesthesia site, and activating a pre-built imaging device and a pre-built spinal fixation device according to the identification instruction;

[0220] Identifying the spine of the patient to be anesthetized, performing a fixation operation on the patient's spine using a spinal fixation device to obtain a fixed spine, and performing a photographing operation on the fixed spine using an imaging device to obtain an original spinal image;

[0221] Performing digital operations on the original spinal image to obtain a standard spinal image;

[0222] Performing a comparison operation on a standard spinal image using a pre-built spinal image database to obtain a plurality of potential anesthesia sites, wherein the spinal image database includes a plurality of reference spinal images, and a distance between a potential anesthesia site and another potential anesthesia site is at least 0.5 cm;

[0223] A spinal image coordinate system is constructed in a standard spinal image, a plurality of potential anesthesia coordinates are obtained based on a plurality of potential anesthesia sites, and the plurality of potential anesthesia coordinates are projected onto a standard spinal image containing the spinal image coordinate system to obtain an optimized spinal image;

[0224] Sending the optimized spinal image to a pre-built spinal management system, wherein the spinal management system includes a spinal diagnosis client corresponding to each of a plurality of spinal diagnosis experts;

[0225] When the spine management system successfully receives the optimized spine image, the spine management system is used to perform a connection operation on the spine diagnosis client. When the spine management system successfully connects to the spine diagnosis client, the spine management system is used to send the optimized spine image to the spine diagnosis client and receive the returned spine image from the spine diagnosis client.

[0226] Summarizing the returned spinal images to obtain a plurality of returned spinal images, and acquiring a plurality of calibrated anesthesia sites based on the plurality of returned spinal images;

[0227] A target anesthesia site is obtained according to a plurality of calibrated anesthesia sites, and the anesthesia site is identified based on the target anesthesia site.

[0228] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 5 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0229] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0230] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0231] receiving an identification instruction of an anesthesia site, and activating a pre-built imaging device and a pre-built spinal fixation device according to the identification instruction;

[0232] Identifying the spine of the patient to be anesthetized, performing a fixation operation on the patient's spine using a spinal fixation device to obtain a fixed spine, and performing a photographing operation on the fixed spine using an imaging device to obtain an original spinal image;

[0233] Performing digital operations on the original spinal image to obtain a standard spinal image;

[0234] Performing a comparison operation on a standard spinal image using a pre-built spinal image database to obtain a plurality of potential anesthesia sites, wherein the spinal image database includes a plurality of reference spinal images, and a distance between a potential anesthesia site and another potential anesthesia site is at least 0.5 cm;

[0235] A spinal image coordinate system is constructed in a standard spinal image, a plurality of potential anesthesia coordinates are obtained based on a plurality of potential anesthesia sites, and the plurality of potential anesthesia coordinates are projected onto a standard spinal image containing the spinal image coordinate system to obtain an optimized spinal image;

[0236] Sending the optimized spinal image to a pre-built spinal management system, wherein the spinal management system includes a spinal diagnosis client corresponding to each of a plurality of spinal diagnosis experts;

[0237] When the spine management system successfully receives the optimized spine image, the spine management system is used to perform a connection operation on the spine diagnosis client. When the spine management system successfully connects to the spine diagnosis client, the spine management system is used to send the optimized spine image to the spine diagnosis client and receive the returned spine image from the spine diagnosis client.

[0238] Summarizing the returned spinal images to obtain a plurality of returned spinal images, and acquiring a plurality of calibrated anesthesia sites based on the plurality of returned spinal images;

[0239] A target anesthesia site is obtained according to a plurality of calibrated anesthesia sites, and the anesthesia site is identified based on the target anesthesia site.

[0240] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0241] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0242] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0243] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0244] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems recited in a system claim may also be implemented by a single unit or system through software or hardware. Second-order terms are used to indicate names and do not imply any particular order.

[0245] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. 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.

Claims

1. A method for identifying anesthesia sites based on image navigation technology, characterized in that: The method comprises: receiving an identification instruction of an anesthesia site, and activating a pre-built imaging device and a pre-built spinal fixation device according to the identification instruction; Identifying the spine of the patient to be anesthetized, performing a fixation operation on the patient's spine using a spinal fixation device to obtain a fixed spine, and performing a photographing operation on the fixed spine using an imaging device to obtain an original spinal image; Performing digital operations on the original spinal image to obtain a standard spinal image; Performing a comparison operation on a standard spinal image using a pre-built spinal image database to obtain a plurality of potential anesthesia sites, wherein the spinal image database includes a plurality of reference spinal images, and a distance between a potential anesthesia site and another potential anesthesia site is at least 0.5 cm; The pre-built spinal image database is used to perform a comparison operation on the standard spinal image to obtain multiple potential anesthesia sites, including: Performing information extraction operations on standard spinal images to obtain standard patient information; The following operations are performed on each of the multiple reference spine images: Acquiring reference patient information based on the reference spinal images, wherein the number of the reference spinal images is the same as the number of the reference patient information, and the dimensions of the standard patient information are the same as the dimensions of the reference patient information, wherein the dimensions of the standard patient information and the dimensions of the reference patient information both include a gender dimension, a weight dimension, an age dimension, a height dimension, and a spinal posture dimension; A reference spine sequence is constructed based on the reference patient information, where the reference spine sequence is as follows: J i =[A、B、C、D、E] Among them, J i represents the reference spine sequence corresponding to the i-th reference spine image in multiple reference spine images, A represents the gender dimension, B represents the weight dimension, C represents the age dimension, D represents the height dimension, and E represents the spine posture dimension; Summarizing the reference vertebral sequences to obtain multiple reference vertebral sequences; A standard spinal sequence is constructed using standard patient information, a plurality of target spinal sequences are identified from a plurality of reference spinal sequences using the standard spinal sequence, and a plurality of first spinal images are extracted from a spinal image database using the plurality of target spinal sequences, wherein the target spinal sequences correspond one-to-one to the first spinal images; Multiple potential anesthesia sites were identified using multiple first spinal images; The method of using a plurality of first spinal images to identify a plurality of potential anesthesia sites includes: A standard spine contour is obtained according to a standard spine image, and the following operation is performed on each of the multiple first spine images: obtaining a first spinal contour according to the first spinal image, constructing a spinal similarity formula, and calculating a spinal similarity value using the spinal similarity formula, the first spinal contour, and a standard spinal contour to obtain a plurality of spinal similarity values; Acquiring a target spinal image based on a plurality of spinal similarity values, wherein the target spinal image is a first spinal image corresponding to a maximum spinal similarity value among the plurality of spinal similarity values; Acquire multiple potential anesthesia sites based on target spinal imaging; The constructing of the spine similarity formula includes: Performing a vertebral body recognition operation on the standard vertebral contour to obtain a plurality of standard vertebral bodies, and identifying a first vertebral body and a second vertebral body from the plurality of standard vertebral bodies; Identify the upper edge of the first vertebra and the lower edge of the second vertebra respectively, and draw tangent lines at the upper edge of the first vertebra and the lower edge of the second vertebra to obtain the first vertebra tangent line and the second vertebra tangent line; Performing a perpendicular line drawing operation on the first vertebral body tangent and the second vertebral body tangent to obtain a first vertebral body perpendicular line and a second vertebral body perpendicular line, and performing a perpendicular line extension operation on the first vertebral body perpendicular line and the second vertebral body perpendicular line to obtain a first extended perpendicular line and a second extended perpendicular line; Obtaining a spinal extension angle according to the first extended perpendicular line and the second extended perpendicular line, performing an angle measurement operation on the spinal extension angle to obtain a spinal angle; Constructing a standard spine matrix based on the weight dimension, age dimension, height dimension and spine intersection angle; Acquire a first intersection angle based on the first spinal image, and summarize the first intersection angles to obtain a plurality of first intersection angles; The following operation is performed on each of the multiple first intersection angles: Constructing a first spine matrix using the first intersection angle, weight dimension, age dimension, and height dimension; The spine similarity formula is constructed using the standard spine matrix and the first spine matrix; The method of constructing a spine similarity formula using the standard spine matrix and the first spine matrix includes: Among them, the standard spine matrix is ​​shown as follows: Among them, Z0 represents the standard spine matrix, α represents the spine intersection angle, β represents the weight dimension, γ represents the age dimension, and δ represents the height dimension; The spine similarity formula is constructed using the standard spine matrix, the first spine matrix and the preset extreme value coefficient, where the spine similarity formula is as follows: Among them, S represents the spine similarity formula, Z i represents the first spine matrix corresponding to the i-th first spine image in multiple first spine images, a represents the extreme value coefficient, || || F represents the Frobenius norm; A spinal image coordinate system is constructed in a standard spinal image, a plurality of potential anesthesia coordinates are obtained based on a plurality of potential anesthesia sites, and the plurality of potential anesthesia coordinates are projected onto a standard spinal image containing the spinal image coordinate system to obtain an optimized spinal image; Sending the optimized spinal image to a pre-built spinal management system, wherein the spinal management system includes a spinal diagnosis client corresponding to each of a plurality of spinal diagnosis experts; When the spine management system successfully receives the optimized spine image, the spine management system is used to perform a connection operation on the spine diagnosis client. When the spine management system successfully connects to the spine diagnosis client, the spine management system is used to send the optimized spine image to the spine diagnosis client and receive the returned spine image from the spine diagnosis client. Summarizing the returned spinal images to obtain a plurality of returned spinal images, and acquiring a plurality of calibrated anesthesia sites based on the plurality of returned spinal images; A target anesthesia site is obtained according to a plurality of calibrated anesthesia sites, and the anesthesia site is identified based on the target anesthesia site.

2. The anesthesia site identification method based on image navigation technology according to claim 1, characterized in that: The digitizing operation is performed on the original spinal image to obtain a standard spinal image, including: Acquiring an initial spinal image based on the original spinal image, performing an upsampling operation on the initial spinal image to obtain an updated spinal image, wherein the updated spinal image includes a plurality of updated pixel points; Performing a first quantization operation on the updated spine image using a preset initial quantization bit number to obtain a plurality of spine pixel points; Acquire a grayscale value set using a plurality of spine pixel points, wherein the grayscale value set includes a plurality of grayscale values, and the grayscale values ​​correspond one-to-one to the spine pixel points; Obtaining a grayscale value variance based on the grayscale value set, wherein the grayscale value variance is a variance of multiple grayscale values ​​in the grayscale value set; Comparing the grayscale value variance with a preset grayscale variance threshold; If the grayscale value variance is less than the grayscale variance threshold, a quantization update operation is performed on the initial quantization bit number using multiple spine pixel points as multiple evaluation pixel points to obtain an updated quantization bit number; Performing a second quantization operation on the plurality of evaluation pixels using the updated quantization bit number to obtain a plurality of target pixels, and performing an encoding operation on the plurality of target pixels to obtain a plurality of encoded pixels; A coded spinal image is obtained according to a plurality of coded pixel points, a coded definition is obtained based on the coded spinal image, and the coded spinal image is optimized using the coded definition to obtain a standard spinal image.

3. The anesthesia site identification method based on image navigation technology according to claim 2, characterized in that: The method of optimizing the encoded spinal image by using encoding clarity to obtain a standard spinal image includes: Compare the encoding clarity with the preset spine clarity threshold; If the coding clarity is greater than or equal to the spine clarity threshold, the coded spine image is used as the standard spine image; If the encoding clarity is less than the spine clarity threshold, a denoising operation is performed on the encoded spine image to obtain a denoised spine image, and a sharpening operation is performed on the denoised spine image to obtain a standard spine image.

4. The anesthesia site identification method based on image navigation technology according to claim 3, characterized in that: The method of projecting a plurality of potential anesthesia coordinates onto a standard spinal image containing a spinal image coordinate system to obtain an optimized spinal image includes: Constructing a target spinal coordinate system using a target spinal image, and confirming the potential anesthesia coordinates in the target spinal coordinate system using a plurality of potential anesthesia coordinates to obtain a plurality of potential spinal coordinates, wherein the potential spinal coordinates correspond one-to-one to the potential anesthesia coordinates; performing a coordinate extraction operation on each of the plurality of potential spine coordinates to obtain a plurality of extracted spine coordinates; Annotating the multiple extracted vertebral coordinates to the vertebral image coordinate system to obtain multiple annotated vertebral coordinates; Performing a labeling operation on each of the plurality of labeled spinal coordinates to obtain a plurality of labeled spinal coordinates, wherein the potential spinal coordinates correspond one-to-one to the labeled spinal coordinates; The standard spine image containing multiple labeled spine coordinates is recorded as the optimized spine image.

5. The anesthesia site identification method based on image navigation technology according to claim 4, characterized in that: The step of obtaining a target anesthesia site according to a plurality of calibrated anesthesia sites includes: Get the number of online clients of the spine diagnosis client; If the number of online clients is greater than or equal to a preset minimum threshold and less than or equal to a preset maximum threshold, repetition rates are calculated for multiple calibrated anesthesia sites to obtain one or more repetition rates; Acquire a target anesthesia site based on the one or more repetition rates, wherein the target anesthesia site is a calibrated anesthesia site corresponding to a maximum repetition rate among the one or more repetition rates; If the number of online clients is greater than the maximum threshold, the calibrated anesthesia sites corresponding to the returned spinal images are sorted in descending order according to the time corresponding to the returned spinal images to obtain a calibrated anesthesia site sequence; Based on the maximum threshold, an initial anesthesia site sequence is selected from the calibrated anesthesia site sequence, wherein the initial anesthesia site sequence includes multiple initial anesthesia sites, and the number corresponding to the initial anesthesia sites is equal to the maximum threshold, and the target anesthesia site is obtained based on the initial anesthesia site sequence.

6. An anesthesia site recognition system based on image navigation technology, characterized in that: The system comprises: An instruction receiving module is used to receive an identification instruction of an anesthesia site and start a pre-built imaging device and a pre-built spinal fixation device according to the identification instruction; Identifying the spine of the patient to be anesthetized, performing a fixation operation on the patient's spine using a spinal fixation device to obtain a fixed spine, and performing a photographing operation on the fixed spine using an imaging device to obtain an original spinal image; A coordinate construction module is used to perform digital operations on the original spinal image to obtain a standard spinal image; Performing a comparison operation on a standard spinal image using a pre-built spinal image database to obtain a plurality of potential anesthesia sites, wherein the spinal image database includes a plurality of reference spinal images, and a distance between a potential anesthesia site and another potential anesthesia site is at least 0.5 cm; The pre-built spinal image database is used to perform a comparison operation on the standard spinal image to obtain multiple potential anesthesia sites, including: Performing information extraction operations on standard spinal images to obtain standard patient information; The following operations are performed on each of the multiple reference spine images: Acquiring reference patient information based on the reference spinal images, wherein the number of the reference spinal images is the same as the number of the reference patient information, and the dimensions of the standard patient information are the same as the dimensions of the reference patient information, wherein the dimensions of the standard patient information and the dimensions of the reference patient information both include a gender dimension, a weight dimension, an age dimension, a height dimension, and a spinal posture dimension; A reference spine sequence is constructed based on the reference patient information, where the reference spine sequence is as follows: J i =[A、B、C、D、E] Among them, J i represents the reference spine sequence corresponding to the i-th reference spine image in multiple reference spine images, A represents the gender dimension, B represents the weight dimension, C represents the age dimension, D represents the height dimension, and E represents the spine posture dimension; Summarizing the reference vertebral sequences to obtain multiple reference vertebral sequences; A standard spinal sequence is constructed using standard patient information, a plurality of target spinal sequences are identified from a plurality of reference spinal sequences using the standard spinal sequence, and a plurality of first spinal images are extracted from a spinal image database using the plurality of target spinal sequences, wherein the target spinal sequences correspond one-to-one to the first spinal images; Multiple potential anesthesia sites were identified using multiple first spinal images; The method of using a plurality of first spinal images to identify a plurality of potential anesthesia sites includes: A standard spine contour is obtained according to a standard spine image, and the following operation is performed on each of the multiple first spine images: obtaining a first spinal contour according to the first spinal image, constructing a spinal similarity formula, and calculating a spinal similarity value using the spinal similarity formula, the first spinal contour, and a standard spinal contour to obtain a plurality of spinal similarity values; Acquiring a target spinal image based on a plurality of spinal similarity values, wherein the target spinal image is a first spinal image corresponding to a maximum spinal similarity value among the plurality of spinal similarity values; Acquire multiple potential anesthesia sites based on target spinal imaging; The constructing of the spine similarity formula includes: Performing a vertebral body recognition operation on the standard vertebral contour to obtain a plurality of standard vertebral bodies, and identifying a first vertebral body and a second vertebral body from the plurality of standard vertebral bodies; Identify the upper edge of the first vertebra and the lower edge of the second vertebra respectively, and draw tangent lines at the upper edge of the first vertebra and the lower edge of the second vertebra to obtain the first vertebra tangent line and the second vertebra tangent line; Performing a perpendicular line drawing operation on the first vertebral body tangent and the second vertebral body tangent to obtain a first vertebral body perpendicular line and a second vertebral body perpendicular line, and performing a perpendicular line extension operation on the first vertebral body perpendicular line and the second vertebral body perpendicular line to obtain a first extended perpendicular line and a second extended perpendicular line; Obtaining a spinal extension angle according to the first extended perpendicular line and the second extended perpendicular line, performing an angle measurement operation on the spinal extension angle to obtain a spinal angle; Constructing a standard spine matrix based on the weight dimension, age dimension, height dimension and spine intersection angle; Acquire a first intersection angle based on the first spinal image, and summarize the first intersection angles to obtain a plurality of first intersection angles; The following operation is performed on each of the multiple first intersection angles: Constructing a first spine matrix using the first intersection angle, weight dimension, age dimension, and height dimension; The spine similarity formula is constructed using the standard spine matrix and the first spine matrix; The method of constructing a spine similarity formula using the standard spine matrix and the first spine matrix includes: Among them, the standard spine matrix is ​​shown as follows: Among them, Z0 represents the standard spine matrix, α represents the spine intersection angle, β represents the weight dimension, γ represents the age dimension, and δ represents the height dimension; The spine similarity formula is constructed using the standard spine matrix, the first spine matrix and the preset extreme value coefficient, where the spine similarity formula is as follows: Among them, S represents the spine similarity formula, Z i represents the first spine matrix corresponding to the i-th first spine image in multiple first spine images, a represents the extreme value coefficient, || || F represents the Frobenius norm; a system management module, configured to construct a spinal image coordinate system in a standard spinal image, obtain a plurality of potential anesthesia coordinates based on a plurality of potential anesthesia sites, and project the plurality of potential anesthesia coordinates into the standard spinal image containing the spinal image coordinate system to obtain an optimized spinal image; Sending the optimized spinal image to a pre-built spinal management system, wherein the spinal management system includes a spinal diagnosis client corresponding to each of a plurality of spinal diagnosis experts; A site determination module is configured to, when the spine management system successfully receives the optimized spine image, use the spine management system to perform a connection operation on the spine diagnosis client; when the spine management system successfully connects to the spine diagnosis client, use the spine management system to send the optimized spine image to the spine diagnosis client, and receive the returned spine image from the spine diagnosis client; Summarizing the returned spinal images to obtain a plurality of returned spinal images, and acquiring a plurality of calibrated anesthesia sites based on the plurality of returned spinal images; A target anesthesia site is obtained according to a plurality of calibrated anesthesia sites, and the anesthesia site is identified based on the target anesthesia site.

Images