CN111134840A - Knee joint replacement surgery scheme generation device and terminal - Google Patents
Knee joint replacement surgery scheme generation device and terminal Download PDFInfo
- Publication number
- CN111134840A CN111134840A CN201911398702.9A CN201911398702A CN111134840A CN 111134840 A CN111134840 A CN 111134840A CN 201911398702 A CN201911398702 A CN 201911398702A CN 111134840 A CN111134840 A CN 111134840A
- Authority
- CN
- China
- Prior art keywords
- knee joint
- bone surface
- dimensional bone
- surface model
- replacement surgery
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
- A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
- A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
- A61B2034/101—Computer-aided simulation of surgical operations
Abstract
The application belongs to the technical field of medical equipment, and particularly relates to a generation device and a terminal of a knee joint replacement surgery scheme, which are used for acquiring a target three-dimensional bone surface model of a femoral head, a knee joint and an ankle joint of a target patient; generating a model by utilizing a pre-constructed joint replacement surgery scheme based on the target three-dimensional bone surface model and outputting a knee joint replacement surgery scheme corresponding to a target patient; in the embodiment of the application, the joint replacement surgery scheme generation model is a model constructed by utilizing knee joint replacement surgery scheme sample data accumulated by experienced doctors, and professional knowledge of the doctors is integrated; therefore, the knee joint replacement surgery scheme output by the joint replacement surgery scheme generation model is the optimal personalized surgery scheme generated according to the individual condition of the patient, is not influenced by the professional level of an individual doctor, and solves the problem that the optimal personalized surgery planning scheme cannot be automatically generated and recommended according to the individual condition of the patient at present.
Description
Technical Field
The application belongs to the technical field of medical equipment, and particularly relates to a generation device and a terminal of a knee joint replacement surgery scheme.
Background
The knee joint is the main load bearing joint of the whole body, the hinge of the lower limb movement, and is also the part easy to wear. The worn knee joint cartilage can not automatically recover, even the varus deformity can occur, so that the wear of the cartilage and the subchondral bone is aggravated, the pain is gradually aggravated, and even the patient can not walk. Total Knee Arthroplasty (TKA) is currently an important approach to treat knee joint disease.
Generally, a doctor can judge whether the knee joint is in a healthy state according to the scanned images and geometric morphological parameters of the femoral head, the knee joint and the ankle joint of a patient; then, determining the structure size (the size of the joint prosthesis), the implantation pose of the joint prosthesis, and the osteotomy amount and the osteotomy plane of the femur and the tibia corresponding to the patient for the knee joint in the unhealthy state; and finally, finishing the knee joint replacement operation according to the determined structure size of the joint prosthesis corresponding to the patient, the implantation pose of the joint prosthesis, the osteotomy amount of the femur and the tibia and the osteotomy plane.
However, the operation plan planning method for determining the structure size of the joint prosthesis, the implantation pose of the joint prosthesis, the osteotomy amount of the femur and the tibia, and the osteotomy plane corresponding to the patient by determining the knee joint state according to the scanned images of the femoral head, the knee joint, and the ankle joint of the patient by the doctor is easily affected by the professional-level individualized differences of the doctor, and there is a problem that an optimal individualized operation plan cannot be automatically generated and recommended according to the personal condition of the patient.
Disclosure of Invention
The embodiment of the application provides a generation device and a terminal of a knee joint replacement surgery scheme, which can solve the problem that an optimal personalized surgery planning scheme cannot be automatically generated and recommended according to the personal condition of a patient at present.
A first aspect of an embodiment of the present application provides an apparatus for generating a knee joint replacement surgery plan, including:
a three-dimensional model acquisition unit for acquiring a target three-dimensional bone surface model of a femoral head, a knee joint and an ankle joint of a target patient;
a knee joint replacement surgery plan generating unit for outputting a knee joint replacement surgery plan corresponding to the target patient by using a pre-constructed knee joint replacement surgery plan generating model based on the target three-dimensional bone surface model; the knee joint replacement surgery plan includes a structural size of a joint prosthesis corresponding to the target patient, an implantation posture of the joint prosthesis, and a resection amount and a resection plane of a femur and a tibia.
A second aspect of the embodiments of the present application provides a method for generating a knee joint replacement surgery plan, including:
acquiring target three-dimensional bone surface models of a femoral head, a knee joint and an ankle joint of a target patient;
outputting a knee joint replacement surgery scheme corresponding to the target patient based on the target three-dimensional bone surface model by using a pre-constructed knee joint replacement surgery scheme generation model; the knee joint replacement surgery plan includes a structural size of a joint prosthesis corresponding to the target patient, an implantation posture of the joint prosthesis, and a resection amount and a resection plane of a femur and a tibia.
A third aspect of the embodiments of the present application provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method when executing the computer program.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the above method.
In the embodiment of the application, a target three-dimensional bone surface model of the femoral head, the knee joint and the ankle joint of a target patient is obtained; outputting a knee joint replacement surgery scheme corresponding to the target patient based on the target three-dimensional bone surface model by utilizing a pre-constructed knee joint replacement surgery scheme generation model; the pre-constructed operation scheme generation model is an operation scheme generation model which is constructed by utilizing knee joint replacement operation scheme sample data (a diseased knee joint three-dimensional bone surface model sample and a prosthesis implanted knee joint three-dimensional bone surface model sample corresponding to each diseased knee joint three-dimensional bone surface model sample in the multiple diseased knee joint three-dimensional bone surface model samples) accumulated by experienced bone surgeons, and professional knowledge of doctors is integrated; therefore, the knee joint replacement surgery scheme output by the pre-constructed knee joint replacement surgery scheme generation model is the surgery scheme which is most matched with the target patient, namely, the optimal personalized surgery scheme is not influenced by the professional level personalized difference of doctors, the problem that the optimal personalized surgery planning scheme cannot be automatically generated and recommended according to the personal condition of the patient at present is solved, the standardization of surgery quality is realized, the generation efficiency of the personalized knee joint replacement surgery scheme can be improved, and the time cost for generating the surgery planning scheme is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic structural diagram of a device for generating a knee joint replacement surgery plan according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a target three-dimensional bone surface model constructed according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a specific implementation process for constructing a target three-dimensional bone surface model according to an embodiment of the present application;
fig. 4 is a schematic diagram of an embodiment of the present application, illustrating automatic generation of a knee replacement surgery plan by a cloud server;
FIG. 5 is a schematic flow chart illustrating a first construction of a knee replacement surgery scenario generation model provided in an embodiment of the present application;
FIG. 6 is a schematic diagram of a second process for constructing a model for generating a knee replacement surgery plan according to an embodiment of the present application;
fig. 7 is a schematic flow chart illustrating an implementation of obtaining sample data according to an embodiment of the present application;
FIG. 8 is a schematic flow chart illustrating an implementation of a method for generating a joint replacement surgical plan according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
The knee joint is the main load bearing joint of the whole body, the hinge of the lower limb movement, and is also the part easy to wear. The worn knee joint cartilage can not automatically recover, even the varus deformity can occur, so that the wear of the cartilage and the subchondral bone is aggravated, the pain is gradually aggravated, and even the patient can not walk. Total Knee Arthroplasty (TKA) is currently an important approach to treat knee joint disease.
Knee arthroplasty is an effective way to restore knee function. Currently, knee replacement mainly includes artificial knee replacement and robot-assisted knee replacement.
The artificial knee joint replacement is mainly realized by mechanical positioning such as an osteotomy template and the like and by means of intramedullary and extramedullary positioning of the femur and the tibia. The accuracy of prosthesis implantation is not only dependent on the skill of the physician in the relevant art, but is also affected by the anatomy of the femur and tibia.
In recent years, with the rapid development of medical imaging technology, digital reality technology, and robotics, robotic-assisted knee replacement has become an important approach to solve the above problems. The knee joint replacement operation performed with the assistance of the robot has obvious advantages in the aspects of operation flexibility, stability, accuracy and the like, can overcome the defects of manual operation, effectively improves the operation precision, improves the operation effect, reduces complications, and promotes the development of the surgical operation to the directions of micro-invasion, intellectualization and accuracy.
However, at present, regardless of the artificial knee joint replacement or the robot-assisted knee joint replacement, whether the knee joint is in a healthy state or not is judged according to the scanned images and geometric morphological parameters of the femoral head, the knee joint and the ankle joint of a patient by depending on the experience of a doctor; then, determining the structure size (the size of the joint prosthesis), the implantation pose of the joint prosthesis, and the osteotomy amount and the osteotomy plane of the femur and the tibia aiming at the knee joint in the unhealthy state; and finally, finishing the knee joint replacement operation according to the determined structure size of the joint prosthesis, the implantation pose of the joint prosthesis, the osteotomy amount of the femur and the tibia and the osteotomy plane.
The operation scheme planning mode of determining the structure size of the joint prosthesis, the implantation pose of the joint prosthesis, the osteotomy amount of the femur and the tibia and the osteotomy plane by judging the state of the knee joint by a doctor according to the scanning images and the geometric morphological parameters of the femoral head, the knee joint and the ankle joint of a patient is easily influenced by professional-level individualized differences of the doctor, and the problem that an optimal individualized operation planning scheme cannot be automatically generated and recommended according to the personal condition of the patient exists.
Based on this, embodiments of the present application provide a device, a method, a terminal, and a computer-readable storage medium for generating a knee joint replacement surgery plan, which can generate an optimal personalized surgery planning plan according to individual conditions of a patient, avoid the surgery planning plan from being affected by professional levels of doctors, achieve standardization of surgery quality, improve generation efficiency of the knee joint replacement surgery plan, and reduce time consumed by surgery.
It should be noted that the present invention provides a device and a method for generating a knee joint replacement surgery plan and a preoperative planning plan for a terminal-output knee joint replacement surgery (knee joint replacement surgery plan) which are applicable not only to knee joint replacement using an artificial knee joint replacement surgery but also to knee joint replacement using a robot-assisted knee joint replacement surgery.
In order to explain the technical means of the present application, the following description will be given by way of specific examples.
As shown in fig. 1, a schematic structural diagram of a generation apparatus 100 for a knee joint replacement surgery plan provided in an embodiment of the present application, the generation apparatus 100 may include a three-dimensional model obtaining unit 101 and a knee joint replacement surgery plan generating unit 102.
The knee joint replacement surgery plan creation apparatus 100 acquires a target three-dimensional bone surface model of a femoral head, a knee joint, and an ankle joint of a target patient by a three-dimensional model acquisition unit 101 disposed in the creation apparatus, and transmits the target three-dimensional bone surface model to a knee joint replacement surgery plan creation unit 102 disposed in the creation apparatus 100; the knee replacement surgery plan generation unit 102 may output a knee replacement surgery plan corresponding to the target patient using a pre-constructed knee replacement surgery plan generation model based on the target three-dimensional bone surface model.
The knee joint replacement surgery plan may include, among other things, the structural size of the joint prosthesis corresponding to the target patient, the implantation pose of the joint prosthesis, and the osteotomy amount and plane of the femur and tibia. Moreover, the pre-constructed operation scheme generation model is constructed by utilizing the sample data of knee joint replacement operation schemes accumulated by experienced surgeons, and the professional knowledge of the surgeons is integrated.
In the embodiment of the application, a target three-dimensional bone surface model of the femoral head, the knee joint and the ankle joint of a target patient is obtained; outputting a knee joint replacement surgery scheme corresponding to the target patient based on the target three-dimensional bone surface model by utilizing a pre-constructed knee joint replacement surgery scheme generation model; the pre-constructed operation scheme generation model is an operation scheme generation model which is constructed by utilizing knee joint replacement operation scheme sample data (a diseased knee joint three-dimensional bone surface model sample and a prosthesis implanted knee joint three-dimensional bone surface model sample corresponding to each diseased knee joint three-dimensional bone surface model sample in the multiple diseased knee joint three-dimensional bone surface model samples) accumulated by experienced surgeons, and is integrated with professional knowledge of the surgeons; therefore, the knee joint replacement surgery scheme output by the pre-constructed knee joint replacement surgery scheme generation model is the surgery scheme which is most matched with the target patient, namely, the optimal personalized surgery scheme is not influenced by the professional level of an individual doctor, the problem that the optimal personalized surgery planning scheme cannot be generated according to the individual condition of the patient at present is solved, the standardization of surgery quality is realized, the generation efficiency of the personalized knee joint replacement surgery scheme can be improved, and the time cost for generating the surgery planning scheme is reduced.
In some embodiments of the present application, as shown in fig. 2, the three-dimensional model obtaining unit 101 may obtain the target three-dimensional bone surface model 22 by obtaining the scanned images 21 of the femoral head, the knee joint and the ankle joint of the target patient in all directions to perform reconstruction of the three-dimensional bone surface model.
Specifically, as shown in fig. 3, the process of acquiring the target three-dimensional bone surface model of the femoral head, the knee joint and the ankle joint of the target patient by the three-dimensional model acquiring unit 101 may include: step 301 to step 302.
The scanning image may include a scanning image obtained by a Computed Tomography (CT) scanning technique or a scanning image obtained by a Magnetic Resonance Imaging (MRI) scanning technique.
Specifically, the target three-dimensional bone surface model of the femoral head, the knee joint and the ankle joint generated according to the scanned image of the femoral head, the knee joint and the ankle joint of the target patient can be generated in a semi-automatic generation mode or a full-automatic generation mode.
The generation of the target three-dimensional bone surface model in the semi-automatic generation mode refers to the steps of segmenting the scanned images of the femoral head, the knee joint and the ankle joint of a target patient through medical image segmentation software carried by a three-dimensional model acquisition unit, acquiring a femoral contour (femoral Label) and a tibial contour (tibial Label) of a removed soft tissue, then acquiring the repairing operation of a doctor on the femoral Label and the tibial Label, obtaining the scanned images for generating the target three-dimensional bone surface model, and generating the target three-dimensional bone surface model based on the scanned images.
The generation of the three-dimensional bone surface model through a full-automatic generation mode refers to inputting the scanned images of the femoral head, the knee joint and the ankle joint of a target patient into a pre-trained deep convolution neural network model, receiving the scanned images segmented by the deep convolution neural network model, and outputting the target three-dimensional bone surface models of the femoral head, the knee joint and the ankle joint.
In some embodiments of the present application, the knee joint replacement surgery plan generating device may be configured in a local server or a cloud server.
As shown in fig. 4, when the knee joint replacement surgery plan generating device is configured in the cloud server, the three-dimensional model obtaining unit in the knee joint replacement surgery plan generating device configured in the cloud server may obtain the scanned images 41 of the femoral head, the knee joint, and the ankle joint of the target patient uploaded by the local client, perform image automatic segmentation according to the scanned images 41 of the femoral head, the knee joint, and the ankle joint of the target patient, generate target three-dimensional bone surface models of the femoral head, the knee joint, and the ankle joint, and transmit the target three-dimensional bone surface models to the knee joint replacement surgery plan generating unit configured in the generating device; and inputting the target three-dimensional bone surface model into a pre-constructed knee joint replacement surgery scheme generation model by a knee joint replacement surgery scheme generation unit, automatically planning a surgery scheme by the pre-constructed surgery scheme generation model to obtain a personalized knee joint replacement surgery scheme corresponding to the target patient, and transmitting the personalized knee joint replacement surgery scheme back to the local client for joint prosthesis implantation simulation.
In some embodiments of the present application, the previously constructed knee replacement surgery scenario generation model may include: a pre-constructed knee joint-prosthesis joint statistical shape model or a pre-trained neural network model.
Specifically, when the previously constructed knee joint replacement surgery plan generation model is a knee joint-prosthesis joint statistical shape model, as shown in fig. 5, the construction of the knee joint-prosthesis joint statistical shape model may include: step 501 to step 504.
In some embodiments of the present application, the post-prosthesis-implantation knee joint three-dimensional bone surface model sample corresponding to each pathologic knee joint three-dimensional bone surface model sample refers to a post-prosthesis-implantation knee joint three-dimensional bone surface model sample obtained by performing surgical planning by an experienced doctor according to the pathologic knee joint three-dimensional bone surface model sample and geometric morphological parameters of a femoral head, a knee joint and an ankle joint of a sample patient.
Specifically, the experienced doctor can obtain the distance between the inner condyle and the outer condyle, the distance between the front condyle and the rear condyle, the size of the tibial plateau, the femoral mechanical shaft and the tibial mechanical shaft of the sample patient according to the three-dimensional bone surface model sample of the pathologic knee joint of the sample patient; secondly, determining the model of the femoral prosthesis according to the distance between the medial condyle and the lateral condyle and the distance between the anterior condyle and the posterior condyle, and determining the model of the tibial prosthesis according to the size of the tibial plateau so as to obtain the structural size of the joint prosthesis of the sample patient; then, determining the implantation pose of the joint prosthesis of the sample patient according to the individualized state of the femur mechanical shaft and the tibia mechanical shaft combined with the sample patient and the professional knowledge of a doctor; and determining the osteotomy amount and the osteotomy plane of the femur and the tibia according to the structure size of the joint prosthesis and the implantation pose of the joint prosthesis to obtain the knee joint three-dimensional bone surface model sample corresponding to the sample patient after the prosthesis is implanted.
And 502, extracting anatomical marking points of each pathologic knee joint three-dimensional bone surface model sample and the corresponding prosthesis implanted knee joint three-dimensional bone surface model sample to obtain an anatomical marking point set of each pathologic knee joint three-dimensional bone surface model sample and an anatomical marking point set of the corresponding prosthesis implanted knee joint three-dimensional bone surface model sample.
It should be noted that, the above-mentioned extracting of the anatomical mark points for each sample of the three-dimensional bone surface model of the pathologic knee joint refers to extracting key points (anatomical mark points) in the sample of the three-dimensional bone surface model of the pathologic knee joint, and the set of the key points can relatively completely represent the sample of the three-dimensional bone surface model of the pathologic knee joint, which is similar to extracting key points of a human face (e.g., a point of an eye position, a point of an eyebrow position) from an image of the human face.
For example, the set of anatomical marker points obtained by extracting N anatomical marker points from the first pathologic knee joint three-dimensional bone surface model sample among the N pathologic knee joint three-dimensional bone surface model samples can be represented as { (x)1，y1)，(x2，y2)，…，(xn，yn) The anatomical mark point set of the three-dimensional bone surface model sample of the knee joint after prosthesis implantation corresponding to the pathologic three-dimensional bone surface model sample of the knee joint can be expressed as { (x'1，y’1)，(x’2，y’2)，…，(x’n，y’n) The joint anatomical marker point set of the pathologic knee joint three-dimensional bone surface model sample and the corresponding knee joint three-dimensional bone surface model sample after prosthesis implantation can be expressed as { (x)1，y1)，(x2，y2)，…，(xn，yn)，(x’1，y’1)，(x’2，y’2)，…，(x’n，y’n) }; the pathologic knee joint three-dimensional bone surface model sample and the shape vector corresponding to the knee joint three-dimensional bone surface model sample after the prosthesis is implanted corresponding to the pathologic knee joint three-dimensional bone surface model sample can be expressed as
Moreover, the N shape vectors corresponding to the N pathologic knee joint three-dimensional bone surface model samples may be represented as:
since the keypoints between these N shape vectors may not be vectors in the same coordinate space, it is necessary to apply to the shape vectors
An alignment transformation is performed such that the N shape vectors are in the same coordinate space.
However, since there is a large information redundancy in the N shape vectors after the alignment transformation, it is also necessary to perform principal component analysis on the N shape vectors after the alignment transformation to implement the N shapesReducing the dimension of the vector, and finally obtaining a model which can be used for expressing the N shape vectors after the alignment transformation
That is, the pre-constructed knee-prosthesis joint statistical shape model that can be used to describe each pathologic knee joint three-dimensional bone surface model sample and its corresponding prosthesis-implanted knee joint three-dimensional bone surface model sample.
Accordingly, after obtaining the pre-constructed knee joint-prosthesis joint statistical shape model, the step of outputting the knee joint replacement surgery plan corresponding to the target patient by using the pre-constructed knee joint replacement surgery plan generation model based on the target three-dimensional bone surface model may include: extracting anatomical marking points of the target three-dimensional bone surface model to obtain an anatomical marking point set of the target three-dimensional bone surface model, and representing the anatomical marking point set of the target three-dimensional bone surface model by using vectors to obtain a shape vector corresponding to the target three-dimensional bone surface model; and then, carrying out alignment transformation on the shape vector corresponding to the target three-dimensional bone surface model to obtain an aligned transformed shape vector corresponding to the target three-dimensional bone surface model, and carrying out elastic registration on the aligned transformed shape vector corresponding to the target three-dimensional bone surface model and the pre-constructed knee joint-prosthesis combined statistical shape model to obtain a knee joint three-dimensional bone surface model after implantation of a target prosthesis corresponding to a three-dimensional bone surface model most similar to the target three-dimensional bone surface model.
Specifically, after the knee joint-prosthesis combined statistical shape model is obtained by training using the plurality of pathologic knee joint three-dimensional bone surface model samples and the knee joint three-dimensional bone surface model sample after prosthesis implantation corresponding to each pathologic knee joint three-dimensional bone surface model sample in the plurality of pathologic knee joint three-dimensional bone surface model samples as a training set, the knee joint-prosthesis combined statistical shape model can derive a large number of new pathologic knee joint three-dimensional bone surface models and the knee joint three-dimensional bone surface model after prosthesis implantation corresponding to the new pathologic knee joint three-dimensional bone surface model, so that after a target three-dimensional bone surface model of a certain patient (target patient) is obtained, the shape vector after alignment transformation corresponding to the target three-dimensional bone surface model of the patient is elastically registered with the knee joint-prosthesis combined statistical shape model, the three-dimensional bone surface model most similar to the target three-dimensional bone surface model and the knee joint three-dimensional bone surface model after implantation of the target prosthesis corresponding to the most similar three-dimensional bone surface model can be obtained.
And after obtaining the three-dimensional bone surface model of the knee joint after the target prosthesis is implanted, the structural size of the joint prosthesis, the implantation pose of the joint prosthesis, the osteotomy amount of the femur and the tibia, and the osteotomy plane corresponding to the target patient are obtained.
In the embodiment of the application, the knee joint three-dimensional bone surface model sample after prosthesis implantation corresponding to the pathologic knee joint three-dimensional bone surface model sample is obtained by performing operation planning by an experienced doctor according to the pathologic knee joint three-dimensional bone surface model sample and the geometrical morphological parameters of the femoral head, the knee joint and the ankle joint of a sample patient, and the professional knowledge of the doctor is integrated; therefore, the knee joint replacement surgery scheme generated by the knee joint-prosthesis joint statistical shape model pre-constructed by the knee joint three-dimensional bone surface model sample after prosthesis implantation corresponding to the pathologic knee joint three-dimensional bone surface model sample is the surgery scheme most matched with the target patient, namely, the optimal personalized surgery scheme is not influenced by the professional level personalized difference of doctors, so that the problem that the optimal personalized surgery planning scheme cannot be automatically generated and recommended according to the personal condition of the patient at present can be solved.
In order to make the knee joint-prosthesis joint statistical shape model lighter and improve the knee joint-prosthesis joint statistical shape model operation efficiency, in some embodiments of the present application, in the construction process of the knee joint-prosthesis joint statistical shape model, parameter value variation of each parameter of the knee joint-prosthesis joint statistical shape model may be detected, and a parameter with the variation smaller than a variation threshold may be removed.
In the embodiment of the application, when the parameter value variation of the parameter is greater than or equal to the variation threshold, it indicates that the parameter is greatly changed in the training process of the knee joint-prosthesis joint statistical shape model, and the parameter is closely related to the output precision of the model, so that the parameter needs to be reserved to ensure the output precision of the model; when the parameter value variation of the parameter is smaller than the variation threshold, the parameter is not changed or is rarely changed in the training process of the knee joint-prosthesis combined statistical shape model, and the output accuracy relation between the parameter and the model is not large, so that the parameter can be removed, the knee joint-prosthesis combined statistical shape model is simplified, the knee joint-prosthesis combined statistical shape model is lighter, and the efficiency of outputting the knee joint replacement surgery scheme by the knee joint-prosthesis combined statistical shape model is improved.
In another embodiment of the present application, as shown in fig. 6, when the knee replacement surgery plan generating model is a neural network model, the constructing of the knee replacement surgery plan generating model may further include: step 601 to step 603.
The knee joint replacement surgery scheme sample data is a surgery scheme obtained by performing surgery planning by experienced doctors according to the pathologic knee joint three-dimensional bone surface model sample and the geometrical morphological parameters of the femoral head, the knee joint and the ankle joint of the sample patient, wherein the knee joint replacement surgery scheme sample data can include the structure size of the joint prosthesis of the sample patient, the implantation pose of the joint prosthesis, the osteotomy amount and the osteotomy plane of the femur and the tibia.
Step 603, calculating the matching degree between the knee joint replacement surgery scheme data corresponding to the pathologic knee joint three-dimensional bone surface model sample output by the to-be-trained knee joint replacement surgery scheme generation model and the knee joint replacement surgery scheme sample data corresponding to each pathologic knee joint three-dimensional bone surface model sample, if the matching degree is smaller than the matching degree threshold value, adjusting the parameters of the to-be-trained knee joint replacement surgery scheme generation model, and training the to-be-trained knee joint replacement surgery scheme generation model by reusing the pathologic knee joint three-dimensional bone surface model sample until the matching degree is larger than or equal to the matching degree threshold value, or when the training times for training the to-be-trained knee joint replacement surgery scheme generation model by reusing the pathologic knee joint three-dimensional bone surface model sample are larger than or equal to the first time threshold value, utilizing the next pathologic knee joint three-dimensional bone surface model sample in the multiple pathologic knee joint replacement surgery scheme generation models to be trained And training the knee joint replacement surgery scheme generation model until the total training times of the knee joint replacement surgery scheme generation model to be trained are greater than or equal to a second time threshold value or the change rate of the matching degree is smaller than a change rate threshold value, and obtaining the pre-constructed knee joint replacement surgery scheme generation model.
The matching degree between the knee joint replacement surgery scheme data corresponding to the pathologic knee joint three-dimensional bone surface model sample output by the to-be-trained knee joint replacement surgery scheme generation model and the knee joint replacement surgery scheme sample data corresponding to the pathologic knee joint three-dimensional bone surface model sample can be calculated by calculating the difference between the structure size of the joint prosthesis in the knee joint replacement surgery scheme data corresponding to the pathologic knee joint three-dimensional bone surface model sample output by the to-be-trained knee joint replacement surgery scheme generation model and the structure size of the second joint prosthesis in the knee joint replacement surgery scheme sample data corresponding to the pathologic knee joint three-dimensional bone surface model sample, and the implantation pose of the joint prosthesis in the knee joint replacement surgery scheme data corresponding to the pathologic knee joint three-dimensional bone surface model sample output by the to-be-trained knee joint replacement surgery scheme generation model and the knee joint replacement surgery scheme corresponding to the pathologic knee joint three-dimensional bone surface model sample The coincidence degree of the implantation pose of the second joint prosthesis in the sample data, the osteotomy amounts of the femur and the tibia in the knee joint replacement surgery scheme data corresponding to the pathologic knee joint three-dimensional bone surface model sample output by the model generated by the knee joint replacement surgery scheme to be trained, the difference value of the osteotomy amounts of the femur and the tibia in the knee joint replacement surgery scheme sample data corresponding to the pathologic knee joint three-dimensional bone surface model sample, and the coincidence degree of the osteotomy planes of the femur and the tibia in the knee joint replacement surgery scheme sample data corresponding to the pathologic knee joint three-dimensional bone surface model sample output by the model generated by the knee joint replacement surgery scheme to be trained, and the osteotomy planes of the femur and the tibia in the knee joint replacement surgery scheme sample data corresponding to the pathologic knee joint three-dimensional bone surface model sample are obtained.
In the embodiment of the application, when the matching degree between the knee joint replacement surgery scheme data corresponding to the pathologic knee joint three-dimensional bone surface model sample output by the to-be-trained knee joint replacement surgery scheme generation model and the knee joint replacement surgery scheme sample data corresponding to the pathologic knee joint three-dimensional bone surface model sample is greater than or equal to the matching degree threshold value, or the training times for re-using the pathologic knee joint three-dimensional bone surface model sample to train the to-be-trained knee joint replacement surgery scheme generation model is greater than or equal to the first time threshold value, the to-be-trained knee joint replacement surgery scheme generation model can output the knee joint replacement surgery scheme of the patient more accurately, when the total training times of the to-be-trained knee joint replacement surgery scheme generation model is greater than or equal to the second time threshold value, or the change rate of the matching degree is less than the change rate threshold value, the accuracy of the knee replacement surgery plan output by the knee replacement surgery plan to be trained tends to be stable, and therefore, it can be represented that the knee replacement surgery plan to be trained has been trained.
In practice, knee replacement surgery protocols are often influenced by individual patient factors, for example, knee replacement surgery protocols for patients with osteoporosis are generally different from knee replacement surgery protocols for patients with normal bone; as another example, knee replacement surgery plans for patients who are professional athletes who need frequent running jumps are generally different from knee replacement surgery plans for patients who are other professions that do not need frequent running jumps.
Therefore, in order to improve the output accuracy of the knee joint-prosthesis joint statistical shape model, in some embodiments of the present application, as shown in fig. 7, the obtaining a three-dimensional bone surface model sample of a diseased knee joint may include: step 701 to step 702.
The personal data sample information of the patient may include: one or more of disease information, ethnicity information, age information, and occupational information relating to bone.
And 702, classifying the three-dimensional bone surface model samples of the diseased knee joint according to the personal data sample information to obtain the three-dimensional bone surface model samples of the diseased knee joint corresponding to the personal data sample information of different categories.
In the embodiment of the application, after the personal data sample information of the patient corresponding to the three-dimensional bone surface model sample of the diseased knee joint is acquired, the three-dimensional bone surface model sample of the diseased knee joint can be classified according to the personal data sample information, and the three-dimensional bone surface model sample of the diseased knee joint corresponding to the personal data sample information of different types is acquired. Therefore, the construction of the knee joint replacement surgery plan generation model may include constructing a three-dimensional bone surface model sample of the diseased knee joint corresponding to the different types of personal data sample information to obtain a pre-constructed knee joint replacement surgery plan generation model corresponding to each type of personal data information.
The same type of personal data sample information has the same or similar characteristics, and the shapes of the knee joints of the patients corresponding to the same type of personal data sample information have the same or similar characteristics, so that the pathologic knee joint three-dimensional bone surface model samples corresponding to the same type of personal data information can be generated into models corresponding to the same pre-constructed knee joint replacement surgery scheme, for example, athletes and couriers need to run and jump frequently, the personal data sample information of the patients who career for the athletes and career for the couriers can be divided into the same type, and the pathologic knee joint three-dimensional bone surface model samples corresponding to the personal data sample information of the patients who career for the athletes and careers for the couriers correspond to the same pre-constructed knee joint replacement surgery scheme generation models; the different types of personal data sample information have different characteristics, and the knee joint forms of the patients corresponding to the different types of personal data sample information are different, so that the diseased knee joint three-dimensional bone surface model samples corresponding to the different types of personal data sample information can correspond to different pre-constructed knee joint replacement surgery schemes to generate models.
Correspondingly, the knee joint replacement surgery plan generating device 100 may further include a model selecting unit configured to obtain the personal data information of the target patient and select a pre-constructed knee joint replacement surgery plan generating model corresponding to the category to which the personal data information belongs.
According to the embodiment of the application, after the target three-dimensional bone surface models of the femoral head, the knee joint and the ankle joint of the target patient are obtained, the pre-constructed knee joint replacement surgery scheme generation model corresponding to the category to which the personal data information belongs can be selected, and the pre-constructed knee joint replacement surgery scheme generation model corresponding to the category to which the personal data information belongs outputs the knee joint replacement surgery scheme corresponding to the target patient, so that the output knee joint replacement surgery scheme can be matched with the individual difference of the patient, the knee joint replacement surgery scheme is more pertinent, and the precision of the preoperative surgery planning scheme of the knee joint replacement surgery is ensured.
In order to further improve the precision of the preoperative surgical planning plan for knee joint replacement surgery, in some embodiments of the present application, the generation device may further include a receiving unit, configured to receive an adjustment operation of the knee joint replacement surgery plan output by the pre-constructed surgery plan generation model, and generate an adjusted knee joint replacement surgery plan according to the adjustment operation.
That is to say, the doctor can adjust the knee joint replacement surgery scheme output by the pre-constructed surgery scheme generation model according to the actual situation, so that the adjusted knee joint replacement surgery scheme generated according to the adjustment operation can better meet the actual situation of the patient, and the precision of the preoperative surgery planning scheme is further improved.
For example, the cloud server can send the output knee joint replacement surgery scheme to the local client, and a doctor can download or check the knee joint replacement surgery scheme recommended by the cloud server on line, adjust the osteotomy back inclination angle, the varus angle, the femoral rotation axis and the osteotomy amount of the femur and the tibia after prosthesis simulation implantation according to actual conditions, further finely adjust the implantation position of the prosthesis, confirm the final knee joint replacement surgery scheme, and apply the scheme to intraoperative navigation and robot operation of the knee joint replacement surgery.
In the above embodiments of the present application, the surgical plan generation model is constructed by using the sample data of the knee joint replacement surgical plan accumulated by experienced surgeons in advance, so that the pre-constructed surgical plan generation model incorporates the professional knowledge of the surgeons; the output knee joint replacement surgery scheme is the surgery scheme which is generated by the pre-constructed knee joint replacement surgery scheme generation model according to the target three-dimensional bone surface models of the femoral head, the knee joint and the ankle joint of the target patient and is most matched with the target patient, the influence of the professional level of an individual doctor can be avoided, the precision of the preoperative surgery planning scheme of the knee joint replacement surgery is effectively guaranteed, the standardization of the surgery quality is realized, the generation efficiency of the knee joint replacement surgery scheme is improved, and the time consumed by the surgery is reduced.
The embodiment of the application also provides a method for generating the knee joint replacement surgery scheme, which is applied to the terminal, can be executed by a device for generating the knee joint replacement surgery scheme configured on the terminal, and is suitable for solving the problem that the optimal personalized surgery planning scheme cannot be generated according to the personal condition of the patient at present. The terminal can be an intelligent terminal such as a cloud server and a local server. As shown in fig. 8, the method for generating a knee replacement surgery plan may include steps 801 to 802.
In some embodiments of the present application, the step 802 may include: acquiring scanning images of a femoral head, a knee joint and an ankle joint of a target patient; and generating a three-dimensional bone surface model of the femoral head, the knee joint and the ankle joint according to the scanned image of the femoral head, the knee joint and the ankle joint of the target patient.
In some embodiments of the present application, before step 802, the method may further include: obtaining a plurality of pathologic knee joint three-dimensional bone surface model samples and a knee joint three-dimensional bone surface model sample after prosthesis implantation corresponding to each pathologic knee joint three-dimensional bone surface model sample in the plurality of pathologic knee joint three-dimensional bone surface model samples; extracting anatomical marking points of each pathologic knee joint three-dimensional bone surface model sample and the corresponding prosthesis implanted knee joint three-dimensional bone surface model sample to obtain an anatomical marking point set of each pathologic knee joint three-dimensional bone surface model sample and an anatomical marking point set of the corresponding prosthesis implanted knee joint three-dimensional bone surface model sample; combining the anatomical mark point set of each pathologic knee joint three-dimensional bone surface model sample and the anatomical mark point set of the knee joint three-dimensional bone surface model sample corresponding to the pathologic knee joint three-dimensional bone surface model sample after prosthesis implantation to obtain a combined anatomical mark point set, and expressing the combined anatomical mark point set by using vectors to obtain each pathologic knee joint three-dimensional bone surface model sample and a shape vector corresponding to the knee joint three-dimensional bone surface model sample after prosthesis implantation corresponding to the pathologic knee joint three-dimensional bone surface model sample; and carrying out alignment transformation on the shape vectors, and carrying out principal component analysis on the shape vectors subjected to alignment transformation to obtain the pre-constructed knee joint-prosthesis joint statistical shape model which can be used for describing each pathologic knee joint three-dimensional bone surface model sample and the corresponding knee joint three-dimensional bone surface model sample after prosthesis implantation.
Accordingly, in some embodiments of the present application, step 802 may further include: extracting anatomical marking points of the target three-dimensional bone surface model to obtain an anatomical marking point set of the target three-dimensional bone surface model, and representing the anatomical marking point set of the target three-dimensional bone surface model by using vectors to obtain a shape vector corresponding to the target three-dimensional bone surface model; and carrying out alignment transformation on the shape vector corresponding to the target three-dimensional bone surface model to obtain an aligned transformed shape vector corresponding to the target three-dimensional bone surface model, and carrying out elastic registration on the aligned transformed shape vector corresponding to the target three-dimensional bone surface model and the pre-constructed knee joint-prosthesis joint statistical shape model to obtain a target prosthesis implanted knee joint three-dimensional bone surface model corresponding to the three-dimensional bone surface model most similar to the target three-dimensional bone surface model.
In some embodiments of the present application, before step 802, the method may further include: detecting parameter value variation of each parameter of the knee joint-prosthesis joint statistical shape model in the construction process of the knee joint-prosthesis joint statistical shape model, and removing the parameter with the variation smaller than a variation threshold.
In some embodiments of the present application, before step 802, the method may further include: acquiring personal data sample information of a patient corresponding to the pathologic knee joint three-dimensional bone surface model sample; classifying the three-dimensional bone surface model samples of the diseased knee joint according to the personal data sample information to obtain three-dimensional bone surface model samples of the diseased knee joint corresponding to the personal data sample information of different categories.
In some embodiments of the present application, before step 802, the method may further include: acquiring personal data information of the target patient, and selecting a pre-constructed knee joint-prosthesis joint statistical shape model corresponding to the category to which the personal data information belongs; the personal profile information includes: one or more of disease information, ethnicity information, age information, and occupational information relating to bone.
In some embodiments of the present application, after step 802, the method may further include: and receiving the adjustment operation of the knee joint replacement surgery scheme output by the pre-constructed surgery scheme generation model, and generating the adjusted knee joint replacement surgery scheme according to the adjustment operation.
It should be noted that, for convenience and simplicity of description, the specific process of the method for generating a knee joint replacement surgery plan may refer to the corresponding working process of the device 100 for generating a knee joint replacement surgery plan described in fig. 1 to 7, and is not described herein again.
As shown in fig. 9, the present application further provides a terminal for implementing a method for generating a knee replacement surgery plan. The terminal 9 may include: a processor 90, a memory 91 and a computer program 92 stored in said memory 91 and executable on said processor 90, such as a knee replacement surgery protocol generation program. The processor 90, when executing the computer program 92, implements the steps in the various knee replacement surgical plan generation method embodiments described above, such as steps 801 through 802 shown in fig. 8. Alternatively, the processor 90, when executing the computer program 92, implements the functions of the modules/units in the above-described device embodiments, such as the functions of the units 101 to 102 shown in fig. 1.
The computer program may be divided into one or more modules/units, which are stored in the memory 91 and executed by the processor 90 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program in the terminal. For example, the computer program may be divided into a three-dimensional model acquisition unit and a knee replacement surgery plan generation unit, each unit functioning specifically as follows:
a three-dimensional model acquisition unit for acquiring a target three-dimensional bone surface model of a femoral head, a knee joint and an ankle joint of a target patient;
a knee joint replacement surgery plan generating unit for outputting a knee joint replacement surgery plan corresponding to the target patient by using a pre-constructed knee joint replacement surgery plan generating model based on the target three-dimensional bone surface model; the knee joint replacement surgery plan includes a structural size of a joint prosthesis corresponding to the target patient, an implantation posture of the joint prosthesis, and a resection amount and a resection plane of a femur and a tibia.
The terminal can be computing equipment such as a local server and a cloud server. The terminal may include, but is not limited to, a processor 90, a memory 91. Those skilled in the art will appreciate that fig. 9 is only an example of a terminal and is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or different components, e.g., the terminal may also include input-output devices, network access devices, buses, etc.
The Processor 90 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 91 may be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The memory 91 may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal. Further, the memory 91 may also include both an internal storage unit and an external storage device of the terminal. The memory 91 is used for storing the computer program and other programs and data required by the terminal. The memory 91 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units 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 units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application 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, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A knee replacement surgical plan generating apparatus, comprising:
a three-dimensional model acquisition unit for acquiring a target three-dimensional bone surface model of a femoral head, a knee joint and an ankle joint of a target patient;
a knee joint replacement surgery plan generating unit for outputting a knee joint replacement surgery plan corresponding to the target patient by using a pre-constructed knee joint replacement surgery plan generating model based on the target three-dimensional bone surface model; the knee joint replacement surgery plan includes a structural size of a joint prosthesis corresponding to the target patient, an implantation posture of the joint prosthesis, and a resection amount and a resection plane of a femur and a tibia.
2. The knee replacement surgery plan generation apparatus of claim 1, wherein the three-dimensional model acquisition unit is specifically configured to:
acquiring scanning images of a femoral head, a knee joint and an ankle joint of a target patient;
and generating a three-dimensional bone surface model of the femoral head, the knee joint and the ankle joint according to the scanned image of the femoral head, the knee joint and the ankle joint of the target patient.
3. The knee replacement surgery plan generation apparatus of claim 1, wherein the pre-constructed knee replacement surgery plan generation model includes: a pre-constructed knee joint-prosthesis joint statistical shape model; the pre-constructed knee joint-prosthesis joint statistical shape model is a joint statistical shape model which is obtained by constructing a diseased knee joint three-dimensional bone surface model sample and a prosthesis implanted knee joint three-dimensional bone surface model sample corresponding to the diseased knee joint three-dimensional bone surface model sample;
the generation apparatus further comprises a model construction unit for:
obtaining a plurality of pathologic knee joint three-dimensional bone surface model samples and a knee joint three-dimensional bone surface model sample after prosthesis implantation corresponding to each pathologic knee joint three-dimensional bone surface model sample in the plurality of pathologic knee joint three-dimensional bone surface model samples;
extracting anatomical marking points of each pathologic knee joint three-dimensional bone surface model sample and the corresponding prosthesis implanted knee joint three-dimensional bone surface model sample to obtain an anatomical marking point set of each pathologic knee joint three-dimensional bone surface model sample and an anatomical marking point set of the corresponding prosthesis implanted knee joint three-dimensional bone surface model sample;
combining the anatomical mark point set of each pathologic knee joint three-dimensional bone surface model sample and the anatomical mark point set of the knee joint three-dimensional bone surface model sample corresponding to the pathologic knee joint three-dimensional bone surface model sample after prosthesis implantation to obtain a combined anatomical mark point set, and expressing the combined anatomical mark point set by using vectors to obtain each pathologic knee joint three-dimensional bone surface model sample and a shape vector corresponding to the knee joint three-dimensional bone surface model sample after prosthesis implantation corresponding to the pathologic knee joint three-dimensional bone surface model sample;
carrying out alignment transformation on the shape vectors, and carrying out principal component analysis on the shape vectors subjected to alignment transformation to obtain the pre-constructed knee joint-prosthesis joint statistical shape model which can be used for describing each pathologic knee joint three-dimensional bone surface model sample and the corresponding knee joint three-dimensional bone surface model sample after prosthesis implantation;
correspondingly, the knee replacement surgery plan generating unit is further specifically configured to:
extracting anatomical marking points of the target three-dimensional bone surface model to obtain an anatomical marking point set of the target three-dimensional bone surface model, and representing the anatomical marking point set of the target three-dimensional bone surface model by using vectors to obtain a shape vector corresponding to the target three-dimensional bone surface model;
and carrying out alignment transformation on the shape vector corresponding to the target three-dimensional bone surface model to obtain an aligned transformed shape vector corresponding to the target three-dimensional bone surface model, and carrying out elastic registration on the aligned transformed shape vector corresponding to the target three-dimensional bone surface model and the pre-constructed knee joint-prosthesis joint statistical shape model to obtain a target prosthesis implanted knee joint three-dimensional bone surface model corresponding to the three-dimensional bone surface model most similar to the target three-dimensional bone surface model.
4. The knee replacement surgery scenario generation apparatus of claim 3, wherein the model construction unit is further configured to:
detecting parameter value variation of each parameter of the knee joint-prosthesis joint statistical shape model in the construction process of the knee joint-prosthesis joint statistical shape model, and removing the parameter with the variation smaller than a variation threshold value.
5. The knee replacement surgery scenario generation apparatus of claim 3, wherein the model construction unit is further configured to:
acquiring personal data sample information of a patient corresponding to the pathologic knee joint three-dimensional bone surface model sample;
classifying the three-dimensional bone surface model samples of the diseased knee joint according to the personal data sample information to obtain three-dimensional bone surface model samples of the diseased knee joint corresponding to the personal data sample information of different categories.
6. The knee replacement surgery plan generation apparatus of claim 5, further comprising a model selection unit; the model selection unit is configured to:
acquiring personal data information of the target patient, and selecting a pre-constructed knee joint-prosthesis joint statistical shape model corresponding to the category to which the personal data information belongs; the personal profile information includes: one or more of disease information, ethnicity information, age information, and occupational information relating to bone.
7. The knee replacement surgery generation apparatus of any one of claims 1-6, further comprising a receiving unit;
the receiving unit is used for receiving the adjustment operation of the knee joint replacement surgery scheme output by the pre-constructed surgery scheme generation model and generating the adjusted knee joint replacement surgery scheme according to the adjustment operation.
8. A method of generating a knee replacement surgical plan, comprising:
acquiring target three-dimensional bone surface models of a femoral head, a knee joint and an ankle joint of a target patient;
outputting a knee joint replacement surgery scheme corresponding to the target patient based on the target three-dimensional bone surface model by using a pre-constructed knee joint replacement surgery scheme generation model; the knee joint replacement surgery plan includes a structural size of a joint prosthesis corresponding to the target patient, an implantation posture of the joint prosthesis, and a resection amount and a resection plane of a femur and a tibia.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method as claimed in claim 8 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method as claimed in claim 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911398702.9A CN111134840B (en) | 2019-12-28 | 2019-12-28 | Knee joint replacement surgery scheme generation device and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911398702.9A CN111134840B (en) | 2019-12-28 | 2019-12-28 | Knee joint replacement surgery scheme generation device and terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111134840A true CN111134840A (en) | 2020-05-12 |
| CN111134840B CN111134840B (en) | 2020-11-20 |
Family
ID=70521984
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911398702.9A Active CN111134840B (en) | 2019-12-28 | 2019-12-28 | Knee joint replacement surgery scheme generation device and terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111134840B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN113116523A (en) * | 2021-04-09 | 2021-07-16 | 元化智能科技(深圳)有限公司 | Orthopedic surgery registration device, terminal equipment and storage medium |
| CN113616350A (en) * | 2021-07-16 | 2021-11-09 | 元化智能科技(深圳)有限公司 | Verification method and device for selected positions of marking points, terminal equipment and storage medium |
| CN113658170A (en) * | 2021-08-30 | 2021-11-16 | 杭州柳叶刀机器人有限公司 | Joint registration point generation method and device, electronic equipment and storage medium |
| WO2022142741A1 (en) * | 2020-12-31 | 2022-07-07 | 北京长木谷医疗科技有限公司 | Total knee arthroplasty preoperative planning method and device |
| CN116942312A (en) * | 2023-09-20 | 2023-10-27 | 中南大学 | Joint replacement operation auxiliary positioning method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1488321A (en) * | 2003-08-21 | 2004-04-14 | 上海交通大学 | Thigh-bone positioning method for full knee-joint replacement operation by robot |
| CN103860294A (en) * | 2014-03-07 | 2014-06-18 | 北京大学第三医院 | Individualized design and manufacturing system and method for full knee joint replacing prosthesis |
| CN104622572A (en) * | 2014-12-15 | 2015-05-20 | 上海昕健医疗技术有限公司 | Individual orthopedics department positioning sheet based on medical images |
| CN108040464A (en) * | 2015-05-28 | 2018-05-15 | 拜欧米特制造有限责任公司 | The complete knee joint scheme neatly planned |
| CN110522501A (en) * | 2019-08-09 | 2019-12-03 | 常州市第二人民医院 | The building of 3D printing personalization orthopedic implant and biomethanics optimized treatment method |
| US10595943B2 (en) * | 2016-12-08 | 2020-03-24 | The Cleveland Clinic Foundation | Model-based surgical planning and implant placement |
-
2019
- 2019-12-28 CN CN201911398702.9A patent/CN111134840B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1488321A (en) * | 2003-08-21 | 2004-04-14 | 上海交通大学 | Thigh-bone positioning method for full knee-joint replacement operation by robot |
| CN103860294A (en) * | 2014-03-07 | 2014-06-18 | 北京大学第三医院 | Individualized design and manufacturing system and method for full knee joint replacing prosthesis |
| CN104622572A (en) * | 2014-12-15 | 2015-05-20 | 上海昕健医疗技术有限公司 | Individual orthopedics department positioning sheet based on medical images |
| CN108040464A (en) * | 2015-05-28 | 2018-05-15 | 拜欧米特制造有限责任公司 | The complete knee joint scheme neatly planned |
| US10595943B2 (en) * | 2016-12-08 | 2020-03-24 | The Cleveland Clinic Foundation | Model-based surgical planning and implant placement |
| CN110522501A (en) * | 2019-08-09 | 2019-12-03 | 常州市第二人民医院 | The building of 3D printing personalization orthopedic implant and biomethanics optimized treatment method |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN113017829B (en) * | 2020-08-22 | 2023-08-29 | 张逸凌 | Preoperative planning method, system, medium and device for total knee arthroplasty based on deep learning |
| WO2022142741A1 (en) * | 2020-12-31 | 2022-07-07 | 北京长木谷医疗科技有限公司 | Total knee arthroplasty preoperative planning method and device |
| CN113116523A (en) * | 2021-04-09 | 2021-07-16 | 元化智能科技(深圳)有限公司 | Orthopedic surgery registration device, terminal equipment and storage medium |
| CN113116523B (en) * | 2021-04-09 | 2022-02-11 | 骨圣元化机器人(深圳)有限公司 | Orthopedic surgery registration device, terminal equipment and storage medium |
| CN113616350A (en) * | 2021-07-16 | 2021-11-09 | 元化智能科技(深圳)有限公司 | Verification method and device for selected positions of marking points, terminal equipment and storage medium |
| CN113658170A (en) * | 2021-08-30 | 2021-11-16 | 杭州柳叶刀机器人有限公司 | Joint registration point generation method and device, electronic equipment and storage medium |
| CN116942312A (en) * | 2023-09-20 | 2023-10-27 | 中南大学 | Joint replacement operation auxiliary positioning method and system |
| CN116942312B (en) * | 2023-09-20 | 2023-12-22 | 中南大学 | Joint replacement operation auxiliary positioning method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111134840B (en) | 2020-11-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111134840B (en) | Knee joint replacement surgery scheme generation device and terminal | |
| US20210338334A1 (en) | Systems and methods for optimizing parameters of orthopaedic procedures | |
| JP6833912B2 (en) | Bone reconstruction and orthopedic implants | |
| US10052206B2 (en) | Deformable articulating templates | |
| US9987092B2 (en) | Computer-assisted joint replacement surgery and patient-specific jig systems | |
| CN101969876B (en) | Customised surgical apparatus | |
| JP2021079142A (en) | Bone reconstruction and orthopedic implants | |
| CN113842211B (en) | Three-dimensional preoperative planning system for knee joint replacement and prosthesis model matching method | |
| CN107106239A (en) | Surgery is planned and method | |
| Gomes et al. | Patient-specific modelling in orthopedics: from image to surgery | |
| US11786308B2 (en) | Orthopaedic implant placement system and method | |
| CN114938995A (en) | Pelvis registration system and medical equipment applied to hip replacement surgery | |
| CA2831458A1 (en) | Customized orthopaedic implants and related methods |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20200512 Assignee: Bone shengyuanhua robot (Shenzhen) Co., Ltd Assignor: Yuanhua Intelligent Technology (Shenzhen) Co.,Ltd. Contract record no.: X2020980004664 Denomination of invention: Generating device and terminal of knee replacement surgery scheme License type: Common License Record date: 20200804 |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220118 Address after: 518000 101, No. 1301-33, sightseeing Road, Xinlan community, Guanlan street, Longhua District, Shenzhen City, Guangdong Province Patentee after: Bone shengyuanhua robot (Shenzhen) Co.,Ltd. Address before: C901, Yinxing technology building, no.1301, sightseeing Road, Xinlan community, Guanlan street, Longhua District, Shenzhen, Guangdong 518000 Patentee before: Yuanhua Intelligent Technology (Shenzhen) Co.,Ltd. |