CN117297769A - Bone layer identification method in hard bone tissue operation - Google Patents

Bone layer identification method in hard bone tissue operation Download PDF

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CN117297769A
CN117297769A CN202311034845.8A CN202311034845A CN117297769A CN 117297769 A CN117297769 A CN 117297769A CN 202311034845 A CN202311034845 A CN 202311034845A CN 117297769 A CN117297769 A CN 117297769A
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段星光
黄俊珲
朱小龙
陈文欣
崔腾飞
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Beijing Institute of Technology BIT
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Abstract

The invention provides a bone layer identification method in hard bone tissue operation, which establishes a finite element simulation model of the hard bone tissue operation; geometrically modeling the hard bone tissue, dispersing the hard bone tissue into finite element grids, defining the material characteristics of the hard bone tissue, and introducing constraint conditions of an operation process into the finite element model; according to different parameters, corresponding interaction force changes are obtained; collecting real-time interaction force signals of a surgical tool and hard bone tissues; selecting a plurality of different types of bone layer features for the interaction force signal; then, a force interaction curve is obtained through a finite element simulation model; establishing a Gaussian mixture model to describe the distribution condition of different bone layer characteristics; by training the network model, different bone layers can be accurately identified. The invention solves the problem of identification of cortical bone and cancellous bone in the process of hard bone tissue surgery.

Description

Bone layer identification method in hard bone tissue operation
Technical Field
The invention provides a bone layer identification method in hard bone tissue surgery, and belongs to the technical field of image processing.
Background
Conventional bone layer identification can provide image information of bone structure by imaging methods such as X-ray, CT scanning and magnetic resonance imaging, but has a certain difficulty in accurately positioning the cortical bone of the inner layer. The close distance between the inner cortical bone and the nerve is often obscured by soft tissue, fat and blood vessels, making accurate identification difficult during surgery. In addition, the position information of the inner cortical bone is automatically or semi-automatically extracted from the medical image based on image processing and computer vision technology. Common methods include edge detection, thresholding, region growing, morphological operations, and the like. These methods can be identified and segmented based on the characteristics, density, and morphology of the bone structure to provide more accurate surgical navigation and localization.
The bone layer identification method based on the image can only be completed before operation, but can not be identified according to signals in real time. Meanwhile, based on the bone layer identification of the image, doctors are required to label one by one, and larger workload is caused.
One of the prior art (Hand-held bone cutting tool with autonomous penetration detection for spinal surgery, IEEE) proposes a Hand-held bone cutting tool system that detects the penetration force of a workpiece. The system learns the cutting state and the moving state from the demonstration of the surgeon, autonomously detects the penetration of the workpiece, and immediately stops the driving of the cutting tool before the complete penetration. The proposed penetration detection scheme does not require knowledge of the shape and position of the workpiece and therefore does not require any expensive systems such as robotic arms and position sensor systems. In addition, the proposed solution can be easily applied to cutting tools of various shapes. The developed system was evaluated experimentally. The results indicate that the developed system performs satisfactorily in both electric and hand-held settings. This technique has the following disadvantages:
1. the method is only suitable for handheld tools, and only distinguishes doctor's operation, but can not realize the identification of bone layers.
2. The surgical tools need to be controllable in real time and therefore need to be customized, which is not satisfactory for use in hospitals.
CN202210471044.7 provides a robot milling chatter type identification method based on power spectrum entropy difference, which comprises the following steps: in the process of robot milling, collecting an original vibration signal of the tail end of the robot; determining the optimal modal decomposition number, and decomposing the original vibration signal into a plurality of sub-signals according to the optimal modal decomposition number; the sub-signal with the center frequency close to the inherent frequency of the cutter-spindle system is marked as a signal B1, and the rest sub-signals lower than the inherent frequency are marked as A1; filtering out spindle frequency conversion and frequency multiplication components in the signals A1 and B1 to obtain signals A2 and B2; respectively calculating the power spectrum entropy of each signal, and further obtaining a power spectrum entropy difference; and determining an optimal classification threshold value of the power spectrum entropy difference, and identifying the flutter type. The invention comprehensively considers the regenerative chatter caused by the flexibility of the cutter-spindle structure and the modal coupling chatter caused by the insufficient rigidity of the robot structure, and realizes the identification of the milling chatter type of the robot. This technique has the following disadvantages:
1. vibration signals are used for identification, the vibration signals are greatly affected by the environment, and meanwhile, effective vibration characteristics are difficult to obtain due to uneven materials of hard bone materials.
2. Vibration signal modal decomposition tends to lose important features.
3. This method is suitable for metal working, and the natural frequency of hard bone tissue is low, so that it cannot be applied to hard bone tissue.
The prior art (Force perception and bone recognition of vertebral lamina milling by robot-assisted ultrasonic bone scalpel based on backpropagation neural network, IEEE) proposes a robotic system that utilizes an ultrasonic bone scalpel to measure lamina milling forces, implementing a safe milling strategy. The developed bone recognition model based on the back propagation neural network is suitable for robot-assisted lamina milling using milling layering and recognition algorithm analysis. The model uses the characteristic milling force, milling speed, milling depth, and ultrasonic scalpel power as inputs to determine whether the milling has reached cortical bone to identify and judge bone layers. The living animal verification experiment shows that the model can accurately determine the safe milling endpoint. Overall, the identification model can significantly improve the safety and reliability of robot-assisted laminectomy, with significant conversion prospects. This technique has the following disadvantages:
1. BP neural networks typically require a large amount of labeling data to train, especially in complex tasks and multi-class classification problems. If the labeling data is limited, the network may have a problem of under fitting, and the information of the data cannot be fully utilized.
2. If the range of the input data varies greatly or is unevenly distributed, the training and performance of the network may be affected. It is often necessary to normalize or normalize the input data to reduce this effect.
The prior art (State recognition of decompressive laminectomy with multiple information in robot-assisted surgery, IEEE) proposes a state recognition system for robotic assisted tele-surgery. By combining the learning method with the conventional method, the slave-side robot can think about the current operation state like a surgeon and provide the master-side surgeon with more information and decision advice, which helps the surgeon work more safely in teleoperation. For windowing, we propose an image-based state recognition method consisting of a U-Net derived network, gray redistribution and dynamic receptive fields, which helps to control the grinding process to prevent the grinding head from damaging the spinal nerves through the inner edge of the blade. Aiming at internal fixation, we propose a state identification method based on audio frequency and force, which consists of a signal characteristic extraction method, LSTM-based prediction and information fusion, and assists in monitoring the drilling process so as to prevent a drill bit from penetrating through the outer edge of the pedicle to damage spinal nerves. This technique has the following disadvantages:
1. with sound and force signals, more sensors are needed.
2. The LSTM prediction information has lower precision, and the prediction range is limited, so that the LSTM prediction information is not suitable for identifying the bone layer.
The prior art (Tactile perception for surgical status recognition in robot-assisted laminectomy, IEEE) proposes a surgical state sensing method using an acceleration sensor and a force sensor mounted on a robot based on a human body tactile sensing mechanism. Introducing a Sinc convolution layer to process the high-frequency vibration signal, and processing the obtained characteristic and the average force signal together by utilizing a one-dimensional convolution network. The method classifies the surgical status into 1 class and outputs a burr blockage probability. Experiments on animal bones verify the effectiveness of the proposed model. In addition, it has been demonstrated that the fusion of the two haptic signals can significantly improve the accuracy of state recognition under varying milling parameters. This technique has the following disadvantages:
1. all data labels are subjective judgment and have a certain difference from the actual situation.
2. Acceleration sensors and force sensors increase the computational effort of the network.
3. The weights of the two signals are not differentiated because the accuracy of the acceleration sensor and the force sensor are not identical.
Disclosure of Invention
The invention provides a bone layer identification method in hard bone tissue operation, which mainly aims to solve the problem of identification of cortical bone and cancellous bone in the hard bone tissue operation process.
The specific technical scheme provided by the invention is as follows:
1. a method for identifying a bone layer in a hard bone tissue operation, comprising the steps of:
step 1, establishing a finite element simulation model of hard bone tissue operation; geometrically modeling the hard bone tissue, dispersing the hard bone tissue into finite element grids, defining the material characteristics of the hard bone tissue, and introducing constraint conditions of an operation process into the finite element model;
step 2, in the simulation process, different operation scenes are considered, and corresponding interaction force changes are obtained according to different parameters;
step 3, performing grinding, drilling and cutting operations by the hard bone tissue operation robot according to the preoperative planning; during the operation, collecting real-time interaction force signals of the surgical tool and the hard bone tissue;
step 4, selecting a plurality of different types of bone layer characteristics for the interaction force signals in order to adapt to different bone materials and different operation parameters; then, a force interaction curve is obtained through a finite element simulation model and is used for guiding labeling of outer cortical bone, cancellous bone and inner cortical bone under different bone layer characteristics;
step 5, establishing a Gaussian mixture model to describe the distribution condition of different bone layer characteristics;
and step 6, training the network model to accurately identify different bone layers. In practical application, the trained network model is used for bone layer identification of the real-time force signals.
Further, the material characteristics of the hard bone tissue in step 1 include elastic modulus, shear modulus, poisson ratio.
The specific method of the step 3 is that a force sensor is arranged at the tail end of the mechanical arm, and an operation tool is arranged on the movable end face of the force sensor; in the operation executing process, the force sensor senses the change of the force signal, and the interaction force signal of the operation tool and the hard bone tissue is acquired by acquiring the signal sensed by the force sensor.
The bone layer characteristics described in step 4 are based on the amplitude, frequency, time domain and frequency domain characteristics of the force signal.
In the step 5, the bone layer features are respectively set into three types of peak force, root mean square force and spectrum features, then the weights and probability density functions of different features are estimated according to the feature distribution conditions, and the weight information of the different bone layer features is obtained by clustering and distribution modeling of feature vectors; and designing and training a neural network model based on the bone layer characteristics established by the marked data set and the Gaussian mixture model.
The neural network model is a deep learning model and comprises a convolutional neural network or a cyclic neural network and is used for learning the association between the force signals and the bone layer categories.
Step 6, performing bone layer identification on the real-time force signals by using the trained network model; by inputting the real-time force signal into the network model, the model will output the corresponding bone layer category.
The invention mainly aims to provide a bone layer identification method for robot assisted hard bone tissue surgery, which solves the problem of shake of various hard bone tissue surgeries; the method has the following technical effects:
1. guiding data annotation by a finite element simulation model, and judging subjectively;
2. the finite element model is suitable for various different hard bone tissues and can be adjusted according to actual operation conditions.
3. Only through force sensor, but not multiple sensor fusion, reduced signal acquisition and processing procedure.
4. And various types of characteristics are established, so that training precision can be improved.
5. And regulating characteristic weights for each group of force signal data through a Gaussian mixture model, and maximally ensuring the effectiveness of the signals.
6. The trained model can be used for real-time bone layer identification and for safety decision-making of the surgical robot.
Drawings
Fig. 1 is a flow chart of the present invention.
Detailed Description
The invention provides a bone layer identification method in hard bone tissue surgery. The whole flow is as shown in figure 1, and the specific steps are as follows:
and step 1, establishing a finite element simulation model of hard bone tissue operation. The hard bone tissue is geometrically modeled and discretized into a finite element mesh, defining the material characteristics of the hard bone tissue, such as elastic modulus, shear modulus, poisson ratio, etc. Constraints of the operating process are introduced in the finite element model.
In the step 2, in the simulation process, different operation situations such as bone cutting, nailing, screw fastening and the like can be considered. The surgical tool and the surgical operation are also provided with different parameters according to actual conditions, such as the diameter and the cutting angle of the surgical tool, the feeding speed, the drilling speed, the feeding angle and the like. And according to different parameters, obtaining corresponding interaction force changes.
And 3, performing grinding, drilling, cutting and other operations by the robot according to preoperative planning. During the execution of the operation, real-time interactive force signals of the surgical tool and the hard bone tissue are acquired. Specifically, the end of the mechanical arm is provided with a force sensor, and the surgical tool is arranged on the movable end face of the force sensor. During the operation execution, the force sensor senses the change of the force signal, and the interaction force signal of the operation tool and the hard bone tissue can be acquired by acquiring the signal sensed by the force sensor.
And 4, selecting a plurality of different types of bone layer characteristics for the interactive force signals to adapt to different bone materials and different operation parameters. These characteristics may be based on amplitude, frequency, time and frequency domain characteristics of the force signal, and the like. For example, peak force, root mean square force, frequency spectral features, time domain statistics, etc. may be selected as bone layer features. And then, a force interaction curve obtained through a finite element simulation model is used for guiding labeling of the outer cortical bone, the cancellous bone and the inner cortical bone under different bone layer characteristics, and the purpose of labeling is to determine corresponding change conditions of different bone layers under different characteristics.
And 5, establishing a Gaussian mixture model (Gaussian Mixture Model, GMM) to describe the distribution condition of different bone layer characteristics. The bone layer features are respectively set into three types of peak force, root mean square force and frequency spectrum features, then the weights and probability density functions of different features are estimated according to the feature distribution conditions, and the weight information of the different bone layer features is obtained by clustering and distribution modeling of feature vectors. And designing and training a proper neural network model based on the bone layer characteristics established by the marked data set and the Gaussian mixture model. This may be a deep learning model, such as a Convolutional Neural Network (CNN) or a Recurrent Neural Network (RNN), for learning the association between the force signal and the bone layer categories.
And step 6, training the network model to accurately identify different bone layers. In practical application, the trained network model is used for bone layer identification of the real-time force signals. By inputting the real-time force signal into the network model, the model will output the corresponding bone layer category. The method can realize real-time bone layer identification and can make corresponding decisions or control according to the requirements.

Claims (7)

1. A method for identifying a bone layer in a hard bone tissue operation, comprising the steps of:
step 1, establishing a finite element simulation model of hard bone tissue operation; geometrically modeling the hard bone tissue, dispersing the hard bone tissue into finite element grids, defining the material characteristics of the hard bone tissue, and introducing constraint conditions of an operation process into the finite element model;
step 2, in the simulation process, different operation scenes are considered, and corresponding interaction force changes are obtained according to different parameters;
step 3, performing grinding, drilling and cutting operations by the hard bone tissue operation robot according to the preoperative planning; during the operation, collecting real-time interaction force signals of the surgical tool and the hard bone tissue;
step 4, selecting a plurality of different types of bone layer characteristics for the interaction force signals in order to adapt to different bone materials and different operation parameters; then, a force interaction curve is obtained through a finite element simulation model and is used for guiding labeling of outer cortical bone, cancellous bone and inner cortical bone under different bone layer characteristics;
step 5, establishing a Gaussian mixture model to describe the distribution condition of different bone layer characteristics;
and step 6, training the network model to accurately identify different bone layers. In practical application, the trained network model is used for bone layer identification of the real-time force signals.
2. The method for identifying bone layers in a hard bone surgery according to claim 1, wherein the material properties of the hard bone in step 1 include elastic modulus, shear modulus, poisson ratio.
3. The method for identifying bone layers in hard bone tissue surgery according to claim 1, wherein the specific method in step 3 is that a force sensor is installed at the tail end of the mechanical arm, and a surgical tool is installed on the movable end face of the force sensor; in the operation executing process, the force sensor senses the change of the force signal, and the interaction force signal of the operation tool and the hard bone tissue is acquired by acquiring the signal sensed by the force sensor.
4. The method of claim 1, wherein the bone layer characteristics of step 4 are based on the amplitude, frequency, time domain and frequency domain characteristics of the force signal.
5. The bone layer identification method in hard bone tissue surgery according to claim 1, wherein in step 5, bone layer features are respectively set as three types of peak force, root mean square force and spectrum features, then weight and probability density functions of different features are estimated according to feature distribution conditions, and weight information of the different bone layer features is obtained by clustering and distribution modeling of feature vectors; and designing and training a neural network model based on the bone layer characteristics established by the marked data set and the Gaussian mixture model.
6. The method for identifying bone layers in hard bone surgery according to claim 5, wherein the neural network model is a deep learning model including a convolutional neural network or a cyclic neural network for learning the association between the force signal and the bone layer class.
7. The method for bone layer identification in hard bone surgery according to claim 1, wherein in step 6, the trained network model is used to perform bone layer identification on the real-time force signal; by inputting the real-time force signal into the network model, the model will output the corresponding bone layer category.
CN202311034845.8A 2023-08-17 2023-08-17 Bone layer identification method in hard bone tissue operation Pending CN117297769A (en)

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