CN114504384B - Knee joint replacement method and device of laser osteotomy robot - Google Patents
Knee joint replacement method and device of laser osteotomy robot Download PDFInfo
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- A61B18/18—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by applying electromagnetic radiation, e.g. microwaves
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Abstract
The invention relates to the field of artificial intelligence, and discloses a knee joint replacement method of a laser osteotomy robot, which comprises the following steps: carrying out image enhancement on the initial imaging image of the knee joint of the user to obtain an enhanced image; detecting a segmentation region in the enhanced image by using a region detection model trained in a laser osteotomy surgical robot in advance, so as to cut the knee joint of the user by using a laser osteotomy device to obtain a cut knee joint, and collecting a cut image of the cut knee joint; identifying a joint replacement region of a cut image by using a target object detection model trained in a laser osteotomy surgical robot in advance, and performing osteotomy on the cut knee joint by using a laser osteotomy device to obtain an osteotomy knee joint; positioning a joint replacement point of an osteotomy knee joint by using a positioning device in a laser osteotomy surgical robot to install a joint prosthesis of a user's knee joint by using a replacement device. The invention can intelligently realize the knee joint replacement operation and can also reduce the risk of the knee joint replacement operation.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a knee joint replacement method and device of a laser osteotomy robot, electronic equipment and a computer readable storage medium.
Background
The knee joint is the most complex load bearing joint in the lower limb, the structure and the function of the knee joint are the most complex of human joints, the existing knee joint replacement operation is usually based on that after a professional doctor resects osteophytes and synovium proliferated in joints of a patient, an extramedullary positioning rod and an osteotomy module are placed in front of a tibia, the tibia is osteotomy is carried out according to the setting of the osteotomy module after the fixation, then, holes are drilled in the femur and the intramedullary positioning rod is inserted, the osteotomy module is installed and fixed, and the subsequent knee joint prosthesis installation is realized after the osteotomy of the distal end of the femur. However, this requires repeated and precise positioning of the osteotomy position, which makes the procedure more cumbersome and consumes more manpower and effort.
In addition, the mechanical osteotomy tool used in the knee joint replacement operation at present is a swing saw which cannot realize accurate bone cutting, and a saw blade can be repeatedly ground when cutting compact and hardened bones, so that adverse phenomena are generated on the cutting of a flat surface; secondly, the osteotomy inevitably produces shaking perpendicular to the osteotomy plane, and the osteotomy surface is uneven, which may increase the risk of failure of the joint prosthesis; meanwhile, when the swing saw repeatedly grinds and cuts hardened bones, overhigh heat can be generated, so that the phenomenon of heat necrosis of the bone surface is caused; the mechanical saw can also cause mechanical damage to bones when cutting bone tissues, destroy the microstructure of the bone tissues, generate a large amount of broken bone fragments, and the broken bone fragments are dead bones, so aseptic necrosis can be caused, the time for cleaning wounds by macrophages is prolonged, the healing time of a bone-prosthesis interface is prolonged, and the phenomenon of blocking bone regeneration can occur. All of these phenomena increase the risk of failure of the knee replacement procedure.
Disclosure of Invention
In order to solve the above technical problems, the present invention provides a knee joint replacement method, apparatus, electronic device and computer readable storage medium for a laser osteotomy robot, which can intelligently implement a knee joint replacement operation and reduce the risk of the knee joint replacement operation.
In a first aspect, the present invention provides a knee joint replacement method for a laser osteotomy robot, comprising:
acquiring an initial iconography image of a knee joint of a user, and performing image enhancement on the initial iconography image to obtain an enhanced image;
detecting a segmentation region in the enhanced image by using a region detection model trained in a laser osteotomy robot in advance;
cutting the knee joint of the user by using a laser osteotomy device in the laser osteotomy surgical robot according to the segmented area to obtain a cut knee joint, and collecting a cut image of the cut knee joint;
recognizing a joint replacement region of the cut image by using a target object detection model trained in a laser osteotomy surgical robot in advance, and performing osteotomy on the cut knee joint by using a laser osteotomy device in the laser osteotomy surgical robot according to the joint replacement region to obtain an osteotomy knee joint;
and positioning a joint replacement point of the osteotomy knee joint by using a positioning device in the laser osteotomy surgical robot, and installing a joint prosthesis of the user knee joint by using a replacement device in the laser osteotomy surgical robot according to the joint replacement point.
In a possible implementation manner of the first aspect, the performing image enhancement on the initial imaging image to obtain an enhanced image includes:
carrying out gray linear enhancement processing on the initial imaging image to obtain a standard image;
performing histogram enhancement on the standard image to obtain a target image;
and denoising the target image to obtain an enhanced image.
In one possible implementation manner of the first aspect, the detecting the segmented region in the enhanced image by using a region detection model trained in a laser osteotomy robot in advance includes:
performing convolution operation on the enhanced image by using a convolution layer in the trained region detection model to obtain a characteristic image;
performing bottom layer feature fusion on the feature image and the initial imagery image by using a fusion layer in the trained region detection model to obtain a fusion image;
performing pooling processing on the fusion image by using a pooling layer in the trained region detection model to obtain a pooled image;
detecting the image area category and the image area coordinate of the pooled image by using a full-connection layer in the trained area detection model;
and outputting the segmentation region in the enhanced image by utilizing an output layer in the trained region detection model according to the image region category and the image region coordinate.
In one possible implementation manner of the first aspect, the cutting the knee joint of the user by using a laser osteotomy device in the laser osteotomy surgical robot according to the segmented region to obtain a cut knee joint includes:
according to the segmentation region, marking a cutting position sequence of the knee joint of the user on the laser osteotomy device;
and starting the laser osteotomy device according to the cutting position sequence to execute cutting on the knee joint of the user to obtain a cut knee joint.
In one possible implementation manner of the first aspect, before identifying the joint replacement region of the cut image by using a target detection model trained in a laser osteotomy robot in advance, the method further includes:
creating a target object detection model to be trained in the laser osteotomy robot, and acquiring a training sample, a real joint replacement region category corresponding to the training sample and a real joint replacement region sequence;
detecting the type of the predicted joint replacement region of the training sample by using a classification network in the target object detection model to be trained, and detecting the sequence of the predicted joint replacement region of the training sample by using a regression network in the target object detection model to be trained;
calculating the model loss of the target object detection model to be trained according to the predicted joint replacement region type, the real joint replacement region type, the predicted joint replacement region sequence and the real joint replacement region sequence;
if the model loss is larger than the preset loss, the step of detecting the predicted joint replacement region category of the training sample by using the classification network in the target object detection model to be trained is returned to after the model parameters of the target object detection model to be trained are adjusted;
and if the model loss is not greater than the preset loss, obtaining a trained target detection model.
In a possible implementation manner of the first aspect, the calculating a model loss of the target object detection model to be trained according to the predicted joint replacement region class and the real joint replacement region class, and the predicted joint replacement region sequence and the real joint replacement region sequence includes:
calculating the model category loss of the target object detection model to be trained according to the predicted joint replacement region category and the real joint replacement region category;
calculating the model sequence loss of the target object detection model to be trained according to the predicted joint replacement region sequence and the real joint replacement region sequence;
and calculating the model loss of the target object detection model to be trained according to the model category loss and the model sequence loss.
In one possible implementation manner of the first aspect, the positioning a joint replacement point of the osteotomy knee joint by using a positioning device in the laser osteotomy surgery robot includes:
measuring a fixed supporting point of the osteotomy knee joint by using the positioning device, and judging whether the fixed supporting point has rejection reaction or not by using the positioning device;
and when the rejection reaction does not occur at the fixed supporting point, taking the fixed supporting point as the joint replacement point.
In a second aspect, the present invention provides a knee joint replacement device for a laser osteotomy robot, the device comprising:
the system comprises an image enhancement module, a data processing module and a data processing module, wherein the image enhancement module is used for acquiring an initial imaging image of a knee joint of a user and enhancing the initial imaging image to obtain an enhanced image;
the segmentation region detection module is used for detecting a segmentation region in the enhanced image by using a region detection model trained in the laser osteotomy robot in advance;
the cutting image acquisition module is used for cutting the knee joint of the user by using a laser osteotomy device in the laser osteotomy surgical robot according to the segmented area to obtain a cut knee joint and acquiring a cutting image of the cut knee joint;
the knee joint osteotomy module is used for identifying a joint replacement region of the cut image by using a target object detection model trained in a laser osteotomy robot in advance, and performing osteotomy on the cut knee joint by using a laser osteotomy device in the laser osteotomy robot according to the joint replacement region to obtain an osteotomy knee joint;
and the joint replacement module is used for positioning a joint replacement point of the osteotomy knee joint by using a positioning device in the laser osteotomy surgical robot, and installing a joint prosthesis of the user knee joint by using a replacement device in the laser osteotomy surgical robot according to the joint replacement point.
In a third aspect, the present invention provides an electronic device comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a method of classroom control of remote multimedia as described in any one of the above first aspects.
In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements a classroom control method for remote multimedia as described in any of the first aspects above.
Compared with the prior art, the technical principle and the beneficial effects of the scheme are as follows:
the embodiment of the invention firstly carries out image enhancement on an initial iconography image of the knee joint of a user to obtain an enhanced image, can enhance the information content of the initial iconography image and improve the accuracy of subsequent image analysis, detects a segmentation region in the enhanced image by using a region detection model trained in a laser osteotomy surgical robot in advance, cuts the knee joint of the user by using a laser osteotomy device to obtain a cut knee joint, collects the cut image of the cut knee joint, can avoid manual positioning by manpower, and realizes intelligent detection of a region to be segmented of the knee joint of the user; secondly, the embodiment identifies the joint replacement area of the cut image by using a target object detection model trained in the laser osteotomy surgical robot in advance, so that the knee joint is cut by using the laser osteotomy device to obtain the osteotomy knee joint, the osteotomy replacement area of the knee joint can be obtained in an intelligent manner, and the joint replacement premise of the subsequent knee joint is guaranteed; furthermore, the embodiment of the invention can realize the automatic and intelligent installation of the knee joint of the user by positioning the joint replacement point of the osteotomy knee joint by using the positioning device in the laser osteotomy surgical robot and installing the joint prosthesis of the knee joint of the user by using the replacement device, and simultaneously can ensure the safety and reliability of the joint replacement of the knee joint of the user. Therefore, the knee joint replacement method, the knee joint replacement device, the electronic device and the computer-readable storage medium of the laser osteotomy robot provided by the embodiment of the invention can intelligently realize the knee joint replacement operation and can also reduce the risk of the knee joint replacement operation.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flowchart illustrating a knee joint replacement method of a laser osteotomy robot according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating one of the steps of a knee replacement method of the laser osteotomy robot of FIG. 1 according to one embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating another step of the knee replacement method of the laser osteotomy robot of FIG. 1 according to one embodiment of the present invention;
FIG. 4 is a block diagram of a knee replacement device of a laser osteotomy robot according to one embodiment of the present invention;
fig. 5 is a schematic internal structural diagram of an electronic device for implementing a knee joint replacement method of a laser osteotomy robot according to an embodiment of the present invention.
Detailed Description
It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a knee joint replacement method of a laser osteotomy surgery robot, and an execution main body of the knee joint replacement method of the laser osteotomy surgery robot comprises at least one of electronic equipment, such as a server and a terminal, which can be configured to execute the method provided by the embodiment of the invention. In other words, the knee joint replacement method of the laser osteotomy surgery robot may be performed by software or hardware installed in a terminal device or a server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a web service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), and a big data and artificial intelligence platform.
Fig. 1 is a schematic flow chart of a knee joint replacement method of a laser osteotomy robot according to an embodiment of the present invention. The knee joint replacement method of the laser osteotomy surgical robot depicted in fig. 1 includes:
s1, acquiring an initial imagery image of a knee joint of a user, and performing image enhancement on the initial imagery image to obtain an enhanced image.
In the embodiment of the invention, the knee joint of the user is a human body joint formed by the lower end of a femur, the upper end of a tibia and a patella, the initial imaging image is a picture obtained by shooting the knee joint of the user through professional medical equipment, and based on the acquisition of the initial imaging image, the tissue condition of the knee joint of the user can be known, so that the premise of positioning a target area in the process of knee joint replacement is ensured.
Furthermore, the embodiment of the invention enhances the information content of the initial imaging image by performing image enhancement on the initial imaging image, thereby improving the accuracy of subsequent image analysis.
As an embodiment of the present invention, the image enhancing the initial imaging image to obtain an enhanced image includes: carrying out gray linear enhancement processing on the initial imagery image to obtain a standard image, carrying out histogram enhancement on the standard image to obtain a target image, and carrying out denoising processing on the target image to obtain an enhanced image.
The gray scale linear enhancement processing of the initial imaging image is used for improving the visual effect of the initial imaging image, the histogram enhancement of the gray scale linear enhancement processing is used for improving the contrast of the initial imaging image, the prominent feature information in the initial imaging image is obtained, and the denoising processing of the target image is used for eliminating the noise in the initial imaging image.
Further, in an optional embodiment of the present invention, the gray scale linear enhancement processing of the initial imaging image is implemented by a linear gray scale enhancement algorithm, the histogram enhancement of the standard image is implemented by a histogram equalization algorithm, and the denoising processing of the target image is implemented by a gaussian filtering algorithm.
And S2, detecting the segmentation region in the enhanced image by using a region detection model trained in the laser osteotomy robot in advance.
According to the embodiment of the invention, the segmented region in the enhanced image is detected by using the region detection model trained in the laser osteotomy robot in advance, so that manual positioning can be avoided, the intelligent detection of the region to be segmented of the knee joint of the user can be realized, and the efficiency of subsequent knee joint replacement can be ensured. The laser osteotomy robot is a machine device capable of autonomously performing knee joint laser osteotomy operation work, the pre-trained region detection model is constructed through a YOLO3 neural network and is used for intelligently detecting a target region image in the image, and the segmentation region is a position sequence region which is used for representing the position sequence region needing to be segmented when the knee joint of a user is subjected to joint switching. In the embodiment of the present invention, the trained region detection model is a model obtained by training a large number of knee joint images, and has a good target region detection capability.
As an embodiment of the present invention, referring to fig. 2, the detecting the segmented region in the enhanced image by using the region detection model trained in the laser osteotomy robot in advance includes:
s201, performing convolution operation on the enhanced image by using a convolution layer in the trained region detection model to obtain a characteristic image;
s202, performing bottom layer feature fusion on the feature image and the initial imaging image by using a fusion layer in the trained region detection model to obtain a fusion image;
s203, performing pooling processing on the fusion image by using a pooling layer in the trained region detection model to obtain a pooled image;
s204, detecting the image area type and the image area coordinate of the pooled image by using a full connection layer in the trained area detection model;
and S205, outputting the segmentation region in the enhanced image by utilizing an output layer in the trained region detection model according to the image region category and the image region coordinate.
The convolution layer is formed by combining a plurality of convolution branch units and is used for extracting abstract features in the enhanced image and guaranteeing the premise of subsequent image classification, the fusion layer is used for performing bottom layer feature fusion on the feature image extracted by the convolution layer and the enhanced image so that the feature image extracted by the convolution layer contains the bottom layer features in the original image and guarantees the accuracy of subsequent image classification, the pooling layer is used for achieving dimension reduction of the image and reducing the calculation complexity of the image, and the full connection layer is used for activating the image information pooled by the pooling layer to obtain the category and the position of the corresponding image.
Further, in this embodiment of the present invention, the image area type indicates whether the detected image is a target image (a segmentation area), which may be represented by 0 and 1, and if the image area type is 0, the detected image is not a target image, and if the image area type is 1, the detected image is a target image, and the image area coordinates refer to a specific position sequence of the detected image, which includes an image center point (x, y), an image length, and an image height.
Further, in an optional embodiment of the present invention, the convolution operation of the enhanced image is implemented by convolution kernels in the convolution layer, the feature image and the bottom-layer feature fusion of the enhanced image are implemented by a feature pyramid in the fusion layer, the pooling process of the fused image is implemented by a pooling function in the pooling layer, such as an average pooling function, and the image area category and the image area coordinate of the pooled image are implemented by an activation function in the fully-connected layer.
And S3, cutting the knee joint of the user by using the laser osteotomy device in the laser osteotomy surgical robot according to the segmented region to obtain a cut knee joint, and collecting a cut image of the cut knee joint.
According to the embodiment of the invention, the knee joint of the user is cut by using the laser osteotomy device in the laser osteotomy surgical robot according to the segmented area so as to obtain the joint tissue information of the knee joint of the user, and the premise of detecting the joint replacement area of the knee joint of the user subsequently is ensured. The laser osteotomy device can be understood as an osteotomy tool in the laser osteotomy surgical robot, and is used for accurately cutting the joint tissue of the knee joint of the user, and it should be noted that, in the invention, the laser osteotomy device realizes the cutting and osteotomy of the knee joint of the user through laser beams, and can realize the functions of two-dimensional and three-dimensional laser cutting, so as to solve the problem that the current knee joint replacement surgical robot cannot perform the intercondylar osteotomy, and can ensure that the laser beams have a thermal coagulation effect on biological tissues while performing the accurate cutting, can close the cut small blood vessels and reduce bleeding, in addition, as the laser beams have high temperature effect, the instrument is not in contact with an excision area, the risk of postoperative infection, especially the infection of an artificial joint interface, and meanwhile, the laser beams do not generate a large amount of splashed bone chips during the cutting, and avoid the infection possibly caused by the rebounded bone chips.
Further, before cutting the knee joint of the user by using the laser osteotomy device in the laser osteotomy surgical robot, the embodiment of the invention further includes: the knee joint of the user is kept in a straightened state, so that the osteotomy complexity in the subsequent knee joint osteotomy process is reduced, and the risk of joint replacement of the subsequent knee joint can be reduced.
As an embodiment of the present invention, referring to fig. 3, the cutting of the knee joint of the user by using the laser osteotomy device in the laser osteotomy surgical robot according to the segmented region to obtain a cut knee joint includes:
s301, according to the segmentation region, marking a cutting position sequence of the knee joint of the user on the laser osteotomy device;
s302, starting the laser osteotomy device according to the cutting position sequence to execute cutting on the knee joint of the user to obtain a cut knee joint.
Further, the embodiment of the present invention collects the cutting image of the cut knee joint to ensure the positioning premise of the joint replacement region of the subsequent user knee joint, wherein the collecting method of the cutting image of the cut knee joint is the same as the collecting method of the initial imaging image of the user knee joint, and is not further described herein.
And S4, recognizing a joint replacement region of the cut image by using a target object detection model trained in the laser osteotomy surgical robot in advance, and performing osteotomy on the cut knee joint by using a laser osteotomy device in the laser osteotomy surgical robot according to the joint replacement region to obtain the osteotomy knee joint.
According to the embodiment of the invention, the joint replacement region of the cutting image is identified by using the target object detection model trained in the laser osteotomy surgical robot in advance, the osteotomy replacement region of the knee joint is obtained in an intelligent manner, and the joint replacement premise of the subsequent knee joint is ensured. The trained target object detection model is constructed through an R-FCN neural network and is used for detecting an image area needing joint replacement in the cutting image.
Further, in an embodiment of the present invention, before the identifying the joint replacement region of the cut image by using the target object detection model trained in the laser osteotomy robot in advance, the method further includes: the method comprises the steps of establishing a target object detection model to be trained in the laser osteotomy surgery robot, obtaining a training sample, a real joint replacement region category and a real joint replacement region sequence corresponding to the training sample, detecting a predicted joint replacement region category of the training sample by using a classification network in the target object detection model to be trained, detecting the predicted joint replacement region sequence of the training sample by using a regression network in the target object detection model to be trained, calculating model loss of the target object detection model to be trained according to the predicted joint replacement region category, the real joint replacement region category, the predicted joint replacement region sequence and the real joint replacement region sequence, adjusting model parameters of the target object detection model to be trained if the model loss is larger than preset loss, returning to the step of detecting the predicted joint replacement region category of the training sample by using the classification network in the target object detection model to be trained, and obtaining the trained target object detection model if the model loss is not larger than the preset loss.
The training sample refers to a cutting image of a historical knee joint, the real joint replacement region category and the real joint replacement region sequence refer to real category and position coordinate information corresponding to the cutting image of the historical knee joint, and the model parameters comprise weight and bias.
Further, in an optional embodiment of the present invention, the target object detection model to be trained may be created in the laser osteotomy robot through Python language, and the training sample may be obtained by querying a professional website.
Further, in an optional embodiment of the present invention, the calculating a model loss of the target object detection model to be trained according to the predicted joint replacement region type, the actual joint replacement region type, the predicted joint replacement region sequence and the actual joint replacement region sequence includes: calculating the model category loss of the target object detection model to be trained according to the predicted joint replacement region category and the real joint replacement region category, calculating the model sequence loss of the target object detection model to be trained according to the predicted joint replacement region sequence and the real joint replacement region sequence, and calculating the model loss of the target object detection model to be trained according to the model category loss and the model sequence loss. Alternatively, the model class loss and the model sequence loss may be calculated by a loss function, such as a cross entropy loss function, a Huber loss function, or the like.
Further, in another optional embodiment of the present invention, the model loss of the target object detection model to be trained is calculated by using the following formula:
wherein the content of the first and second substances,
the loss of the model is represented by,
the loss of the model class is represented,
representing the loss of the sequence of the model,
， 
loss weights representing model class losses and model sequence losses.
Further, in another optional embodiment of the present invention, the preset loss may be set to 0.1, or may be set according to an actual service scenario, and the model parameter of the target detection model to be trained may be adjusted by a gradient descent algorithm, such as a random gradient descent algorithm.
Further, according to the joint replacement region, the cut knee joint is cut by the laser bone cutting device in the laser bone cutting operation robot to obtain the cut knee joint, so that automatic bone cutting of the joint to be replaced in the knee joint of the user is realized, and the premise of subsequent joint replacement is guaranteed.
It should be noted that the osteotomy for cutting the knee joint is the same as the cutting implementation principle for the knee joint of the user, and further description is omitted here.
And S5, positioning a joint replacement point of the osteotomy knee joint by using a positioning device in the laser osteotomy surgical robot, and installing a joint prosthesis of the user knee joint by using a replacement device in the laser osteotomy surgical robot according to the joint replacement point.
According to the embodiment of the invention, the positioning device in the laser osteotomy surgical robot is utilized to position the joint replacement point of the osteotomy knee joint so as to determine the subsequent mounting point of the joint prosthesis, wherein the positioning device is a tool for automatically identifying the joint replacement mounting point.
As an embodiment of the present invention, the positioning of the joint replacement point of the osteotomy knee joint using the positioning device in the laser osteotomy surgical robot includes: the positioning device is utilized to measure the fixed supporting point of the osteotomy knee joint, the positioning device is utilized to judge whether the fixed supporting point has rejection reaction, and the fixed supporting point is taken as the joint replacement point when the rejection reaction does not occur at the fixed supporting point.
The fixed supporting point is used for supporting the subsequent joint prosthesis to be stably installed on the knee joint of the user, and the rejection reaction refers to whether the subsequent joint prosthesis is installed on the joint replacement point and is in rejection reaction with the knee joint of the user.
Furthermore, according to the embodiment of the invention, the replacement device in the laser osteotomy surgical robot is used for installing the joint prosthesis of the user knee joint according to the joint replacement point, so that the automatic and intelligent installation of the user knee joint is realized, and meanwhile, the safety and reliability of the joint replacement of the user knee joint can be ensured.
It can be seen that, in the embodiment of the present invention, an initial imaging image of a user's knee joint is first image-enhanced to obtain an enhanced image, which can enhance the information content of the initial imaging image and improve the accuracy of subsequent image analysis, and a segmentation region in the enhanced image is detected by using a region detection model trained in a laser osteotomy surgical robot in advance, so that the user's knee joint is cut by using a laser osteotomy device to obtain a cut knee joint, and the cut image of the cut knee joint is collected, thereby avoiding manual positioning by manpower and realizing intelligent detection of a region to be segmented of the user's knee joint; secondly, the embodiment identifies the joint replacement area of the cut image by using a target object detection model trained in the laser osteotomy surgical robot in advance, so that the knee joint is cut by using the laser osteotomy device to obtain the osteotomy knee joint, the osteotomy replacement area of the knee joint can be obtained in an intelligent manner, and the joint replacement premise of the subsequent knee joint is guaranteed; furthermore, the embodiment of the invention can realize the automatic and intelligent installation of the knee joint of the user by positioning the joint replacement point of the osteotomy knee joint by using the positioning device in the laser osteotomy surgical robot and installing the joint prosthesis of the knee joint of the user by using the replacement device, and simultaneously can ensure the safety and reliability of the joint replacement of the knee joint of the user. Therefore, the knee joint replacement method of the laser osteotomy robot provided by the embodiment of the invention can intelligently realize the knee joint replacement operation and can reduce the risk of the knee joint replacement operation.
Fig. 4 is a functional block diagram of a knee joint replacement device of a laser osteotomy robot according to the present invention.
The knee joint replacement device 400 of the laser osteotomy robot of the present invention may be installed in an electronic device. According to the realized functions, the knee joint replacement device of the laser osteotomy surgery robot may include an image enhancement module 401, a segmented region detection module 402, a cut image acquisition module 403, a knee joint osteotomy module 404, and a joint replacement module 405. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, and are stored in a memory of the electronic device.
In the embodiment of the present invention, the functions of the modules/units are as follows:
the image enhancement module 401 is configured to collect an initial imaging image of a knee joint of a user, and perform image enhancement on the initial imaging image to obtain an enhanced image;
the segmented region detection module 402 is configured to detect a segmented region in the enhanced image by using a region detection model trained in a laser osteotomy robot in advance;
the cutting image acquisition module 403 is configured to cut the knee joint of the user by using a laser osteotomy device in the laser osteotomy surgical robot according to the segmented area to obtain a cut knee joint, and acquire a cutting image of the cut knee joint;
the knee joint osteotomy module 404 is configured to identify a joint replacement region of the cut image by using a target object detection model trained in a laser osteotomy robot in advance, and perform osteotomy on the cut knee joint by using a laser osteotomy device in the laser osteotomy robot according to the joint replacement region to obtain an osteotomy knee joint;
the joint replacement module 405 is configured to position a joint replacement point of the osteotomy knee joint by using a positioning device in the laser osteotomy surgical robot, and mount a joint prosthesis of the user knee joint by using a replacement device in the laser osteotomy surgical robot according to the joint replacement point.
In detail, when the knee joint replacement device 400 of the laser osteotomy robot in the embodiment of the present invention is used, the same technical means as the knee joint replacement method of the laser osteotomy robot described in fig. 1 to 3 is adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a knee joint replacement method of a laser osteotomy robot according to the present invention.
The electronic device may include a processor 50, a memory 51, a communication bus 52, and a communication interface 53, and may further include a computer program stored in the memory 51 and executable on the processor 50, such as a knee replacement program of a laser osteotomy robot.
In some embodiments, the processor 50 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 50 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing a program or a module (e.g., executing a knee joint replacement program of a laser osteotomy robot, etc.) stored in the memory 51 and calling data stored in the memory 51.
The memory 51 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 51 may in some embodiments be an internal storage unit of the electronic device, e.g. a removable hard disk of the electronic device. The memory 51 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device. Further, the memory 51 may also include both an internal storage unit and an external storage device of the electronic device. The memory 51 may be used not only to store application software installed in an electronic device and various types of data, such as codes of a knee joint replacement program of a laser osteotomy robot, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 52 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 51 and at least one processor 50 or the like.
The communication interface 53 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 5 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 5 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to the various components, and preferably, the power supply may be logically connected to the at least one processor 50 through a power management device, so that functions such as charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the embodiments described are for illustrative purposes only and that the scope of the claimed invention is not limited to this configuration.
The memory 51 in the electronic device stores a knee joint replacement program of the laser osteotomy surgery robot, which is a combination of a plurality of computer programs that, when executed in the processor 50, can implement:
acquiring an initial iconography image of a knee joint of a user, and performing image enhancement on the initial iconography image to obtain an enhanced image;
detecting a segmentation region in the enhanced image by using a region detection model trained in a laser osteotomy robot in advance;
cutting the knee joint of the user by using a laser osteotomy device in the laser osteotomy surgical robot according to the segmented area to obtain a cut knee joint, and collecting a cut image of the cut knee joint;
recognizing a joint replacement region of the cut image by using a target object detection model trained in a laser osteotomy surgical robot in advance, and performing osteotomy on the cut knee joint by using a laser osteotomy device in the laser osteotomy surgical robot according to the joint replacement region to obtain an osteotomy knee joint;
and positioning a joint replacement point of the osteotomy knee joint by using a positioning device in the laser osteotomy surgical robot, and installing a joint prosthesis of the user knee joint by using a replacement device in the laser osteotomy surgical robot according to the joint replacement point.
Specifically, the processor 50 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor of an electronic device, implements:
acquiring an initial iconography image of a knee joint of a user, and performing image enhancement on the initial iconography image to obtain an enhanced image;
detecting the segmentation region in the enhanced image by using a region detection model trained in a laser osteotomy robot in advance;
cutting the knee joint of the user by using a laser osteotomy device in the laser osteotomy surgical robot according to the segmented area to obtain a cut knee joint, and collecting a cut image of the cut knee joint;
identifying a joint replacement region of the cut image by using a target object detection model trained in a laser osteotomy surgical robot in advance, and cutting the cut knee joint by using a laser osteotomy device in the laser osteotomy surgical robot according to the joint replacement region to obtain an osteotomy knee joint;
and positioning a joint replacement point of the osteotomy knee joint by using a positioning device in the laser osteotomy surgical robot, and installing a joint prosthesis of the user knee joint by using a replacement device in the laser osteotomy surgical robot according to the joint replacement point.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The above description is merely illustrative of particular embodiments of the invention that enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (1)
1. A knee replacement device of a laser osteotomy robot, the device comprising:
the image enhancement module is used for acquiring an initial iconography image of the knee joint of the user and carrying out image enhancement on the initial iconography image to obtain an enhanced image;
the segmented region detection module is used for detecting segmented regions in the enhanced image by using a region detection model which is trained in the laser osteotomy robot in advance;
the cutting image acquisition module is used for cutting the knee joint of the user by using a laser osteotomy device in the laser osteotomy surgical robot according to the segmented area to obtain a cut knee joint and acquiring a cutting image of the cut knee joint;
the knee joint osteotomy module is used for identifying a joint replacement region of the cut image by using a target object detection model trained in a laser osteotomy robot in advance, and performing osteotomy on the cut knee joint by using a laser osteotomy device in the laser osteotomy robot according to the joint replacement region to obtain an osteotomy knee joint;
before the step of identifying the joint replacement region of the cut image by using the target object detection model trained in the laser osteotomy robot in advance, the method further comprises the following steps:
creating a target object detection model to be trained in the laser osteotomy robot, and acquiring a training sample, a real joint replacement region category corresponding to the training sample and a real joint replacement region sequence;
detecting the type of the predicted joint replacement region of the training sample by using a classification network in the target object detection model to be trained, and detecting the predicted joint replacement region sequence of the training sample by using a regression network in the target object detection model to be trained;
calculating the model loss of the target object detection model to be trained according to the predicted joint replacement region type, the real joint replacement region type, the predicted joint replacement region sequence and the real joint replacement region sequence;
if the model loss is larger than the preset loss, the step of detecting the predicted joint replacement region category of the training sample by using the classification network in the target object detection model to be trained is returned to after the model parameters of the target object detection model to be trained are adjusted;
if the model loss is not larger than the preset loss, obtaining a trained target detection model;
and the joint replacement module is used for positioning a joint replacement point of the osteotomy knee joint by using a positioning device in the laser osteotomy surgical robot, and installing a joint prosthesis of the user knee joint by using a replacement device in the laser osteotomy surgical robot according to the joint replacement point.
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| CN110163219A (en) * | 2019-04-17 | 2019-08-23 | 安阳师范学院 | Object detection method based on image border identification |
| CN112842521A (en) * | 2021-02-09 | 2021-05-28 | 中国人民解放军陆军军医大学第二附属医院 | Laser cuts supplementary full knee joint replacement surgery robot of bone |
| CN113017829A (en) * | 2020-08-22 | 2021-06-25 | 张逸凌 | Preoperative planning method, system, medium and equipment for total knee replacement based on deep learning |
| CN113662660A (en) * | 2021-10-22 | 2021-11-19 | 杭州键嘉机器人有限公司 | Joint replacement preoperative planning method, device, equipment and storage medium |
| WO2021237279A1 (en) * | 2020-05-25 | 2021-12-02 | Orthopaedic Innovations Pty Ltd | A surgical method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110163219A (en) * | 2019-04-17 | 2019-08-23 | 安阳师范学院 | Object detection method based on image border identification |
| WO2021237279A1 (en) * | 2020-05-25 | 2021-12-02 | Orthopaedic Innovations Pty Ltd | A surgical method |
| CN113017829A (en) * | 2020-08-22 | 2021-06-25 | 张逸凌 | Preoperative planning method, system, medium and equipment for total knee replacement based on deep learning |
| CN112842521A (en) * | 2021-02-09 | 2021-05-28 | 中国人民解放军陆军军医大学第二附属医院 | Laser cuts supplementary full knee joint replacement surgery robot of bone |
| CN113662660A (en) * | 2021-10-22 | 2021-11-19 | 杭州键嘉机器人有限公司 | Joint replacement preoperative planning method, device, equipment and storage medium |
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