CN117132632B - Image registration method, device, computer readable storage medium and computer equipment - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000011524 similarity measure Methods 0.000 claims abstract description 16
- 238000004364 calculation method Methods 0.000 claims abstract description 12
- 238000004590 computer program Methods 0.000 claims description 15
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 238000005457 optimization Methods 0.000 claims description 8
- 238000003384 imaging method Methods 0.000 claims description 7
- 238000010845 search algorithm Methods 0.000 claims description 7
- 238000001356 surgical procedure Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 3
- 210000000988 bone and bone Anatomy 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000000399 orthopedic effect Effects 0.000 description 2
- 238000002432 robotic surgery Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
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- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/761—Proximity, similarity or dissimilarity measures
-
- 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
- A61B2034/105—Modelling of the patient, e.g. for ligaments or bones
-
- 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
- A61B2034/107—Visualisation of planned trajectories or target regions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30008—Bone
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The application is applicable to surgical robots and provides an image registration method, an image registration device, a computer-readable storage medium and computer equipment. The method comprises the following steps: s101, when similarity measure does not reach a preset extremum in the image registration process, acquiring a six-dimensional initial pose and a six-dimensional pose change range of a preset three-dimensional image of a patient before operation; s102, setting an approximation value, searching each dimension pose, and searching an optimal solution; and S103, carrying out similarity calculation on the generated DRR projection and the corresponding X-ray image after carrying out translation rotation on the three-dimensional model according to the change value of each current dimension, and outputting a pose POS after optimizing search if the difference value is smaller than the current set value. The method and the device can rapidly calculate the extremum, reduce the registration time and improve the registration efficiency.
Description
Technical Field
The application belongs to the field of surgical robots, and particularly relates to an image registration method, an image registration device, a computer-readable storage medium and computer equipment.
Background
In the field of bone surgery, traditional manual open surgery is being replaced by robotic surgery, and traditional bone surgery is entering the minimally invasive age of robotic surgery. In the orthopedic surgery robot technology, the registration technology completed before surgery is one of the core technologies, namely, three-dimensional images of a patient before surgery, such as CT and the like, are reconstructed into a three-dimensional model in a computer, then a virtual two-dimensional image is generated by utilizing the DRR (Digitally Reconstructured Radiograph) technology, and a doctor can perform surgery planning on the three-dimensional model or the virtual two-dimensional image.
The conventional registration method includes the steps of: performing three-dimensional reconstruction before operation, and then shooting X-rays (virtual X-ray machine) on the reconstructed three-dimensional model by using a DRR technology to obtain a virtual positive side two-dimensional image; shooting an actual operation area by using a C-arm X-ray machine in the operation, acquiring a positive side two-dimensional image and preprocessing; and then, performing similarity measurement technology (namely registration) on the intra-operative positive side two-dimensional image and the virtual positive side two-dimensional image acquired before the operation, and finishing the registration when the similarity measurement value reaches a preset extremum, otherwise, performing an optimization strategy on the reconstructed three-dimensional model. The optimization strategy is as follows: firstly, rotating a reconstructed three-dimensional model by an angle, then acquiring a virtual positive side two-dimensional image, and then carrying out similarity measure value calculation with the intraoperative positive side two-dimensional image to judge whether a preset extremum is reached; and so on.
Common optimization strategies are nonlinear optimization (NLOPT), powell's algorithm (Powell), and so on. NLOPT (nonlinear optimization, nonlinear optimization library) is a free open-source library, provides a use interface for a plurality of nonlinear optimization algorithms, and cannot be matched with the 2D and 3D registration of the orthopedics images, so that the method is not suitable for the registration of the orthopaedics images. The Powell algorithm (Powell) is a commonly used acceleration algorithm in image registration, can accelerate the search speed, and has good effect on low-dimensional functions. In the aspect of 2D and 3D registration of the orthopaedics images, the method can be matched, but the algorithm is carried out with more time consumption and lower efficiency.
It can be seen that the registration efficiency and the registration time mainly depend on whether the extremum can be quickly obtained between the 2D image in the operation and the 2D image generated based on three dimensions before the operation.
Disclosure of Invention
The invention aims to provide an image registration method, an image registration device, a computer-readable storage medium and computer equipment, which can quickly calculate extremum, reduce registration time and improve registration efficiency.
In a first aspect, the present application provides an image registration method comprising the steps of:
s101, when similarity measure does not reach a preset extremum in the image registration process, acquiring a six-dimensional initial pose and a six-dimensional pose change range of a preset three-dimensional image of a patient before operation;
s102, setting an approximation value, searching each dimension pose, and searching an optimal solution;
and S103, carrying out similarity calculation on the generated DRR projection and the corresponding X-ray image after carrying out translation rotation on the three-dimensional model according to the change value of each current dimension, and outputting a pose POS after optimizing search if the difference value is smaller than the current set value.
In a second aspect, the present application provides an image registration apparatus, the apparatus comprising:
the acquisition module is used for acquiring a six-dimensional initial pose and a six-dimensional pose change range of a pre-operation three-dimensional image of a patient when the similarity measure does not reach a preset extremum in the image registration process;
the search module is used for setting an approximation value, searching each dimension pose and searching for an optimal solution;
and the termination search module is used for carrying out the sum of the DRR projection generated after the translation rotation of the three-dimensional model and the corresponding X-ray image according to the change value of each current dimension, carrying out the similarity calculation, and outputting the pose POS after optimizing the search if the difference value is smaller than the current set value.
In a third aspect, the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of an image registration method as described above.
In a fourth aspect, the present application provides a computer device comprising:
one or more processors;
a memory; and one or more computer programs, the processor and the memory being connected by a bus, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, which when executing the computer programs, implement the steps of the image registration method as described above.
In the application, when the similarity measure does not reach a preset extremum in the image registration process, acquiring a six-dimensional initial pose and a six-dimensional pose change range of a preset three-dimensional image of a patient before operation; setting an approximation value, searching each dimension pose, and searching for an optimal solution; and carrying out the parallel movement and rotation on the three-dimensional model according to the current change value of each dimension to generate a sum of the DRR projection and the corresponding X-ray image after similarity calculation, and if the difference is smaller than the current set value, optimizing search and outputting the pose POS. Therefore, the extremum can be calculated rapidly, the registration time is reduced, and the registration efficiency is improved.
Drawings
Fig. 1 is a flowchart of an image registration method according to an embodiment of the present application.
Fig. 2 is a functional block diagram of an image registration apparatus according to an embodiment of the present application.
Fig. 3 is a specific structural block diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantageous effects of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In order to illustrate the technical solutions described in the present application, the following description is made by specific examples.
Referring to fig. 1, a flowchart of an image registration method according to an embodiment of the present application is mainly exemplified by application of the image registration method to a computer device, and the image registration method according to an embodiment of the present application includes the following steps:
s101, when the similarity measure does not reach a preset extremum in the image registration process, acquiring a six-dimensional initial pose and a six-dimensional pose change range of a preset three-dimensional image of the patient before operation.
The six-dimensional initial pose POS is specifically POS= { TX, TY, TZ, RX, RY, RZ }, TX, TY and TZ are translation amounts along an X coordinate axis, a Y coordinate axis and a Z coordinate axis respectively, RX, RY and RZ are rotation angles around the X coordinate axis, the Y coordinate axis and the Z coordinate axis respectively, and the six-dimensional pose change range RNG is specifically RNG= { RTX, RTY, RTZ, RRX, RRY, RRZ }, and RTX, RTY, RTZ, RRX, RRY and RRZ are change ranges of TX, TY, TZ, RX, RY and RZ respectively.
S102, setting an approximation value, searching each dimension pose, and searching for an optimal solution.
In an embodiment of the present application, a binary search algorithm or a golden section search algorithm may be used to search for each pose, and the golden section search algorithm is described below as an example. The approximation DELTA is set by an artificial empirical value, and the approximation delta=0.005 is set.
S102 may specifically include the following steps:
s1021, start=pos [ i ] -0.382 x RNG [ i ], end=pos [ i ] +0.618 x RNG [ i ], where POS [ i ] is each dimension pose, RNG [ i ] is a variation range of each dimension pose, RNG [ i ] (i=0, 1,2, … 5), POS [ i ] (i=0, 1,2, … 5);
s1022, step len= (END-START)/1.618, LEFT END left=start+len, RIGHT END right=end-LEN,
let POS [ i ] =LEFT, do DRR projection, calculate the similarity with X-ray image to obtain the first similarity SL,
making POS [ i ] =RIGHT, making DRR projection, calculating similarity with X-ray image to obtain second similarity SR,
end=left if SL > SR, else start=right;
s1023, repeating S1022 until |left-right| < = DELTA returns;
return value ret= (left+right)/2;
s1024, calculating the similarity Si between the pose POS [ i ] and the corresponding X-ray image after DRR projection and the similarity Sn between the pose POS [ i ] and the corresponding X-ray image after DRR projection when POS [ i ] =RET, wherein the maximum value of the similarity is Smax; smax=si if Si > Sn, otherwise smax=sn, POS [ i ] =ret;
and S103, carrying out similarity calculation on the generated DRR projection and the corresponding X-ray image after carrying out translation rotation on the three-dimensional model according to the change value of each current dimension, and outputting a pose POS after optimizing search if the difference value is smaller than the current set value.
In an embodiment of the present application, the pose POS is a current virtual space pose where a reconstructed three-dimensional image is located, and the three-dimensional model is a three-dimensional model of a patient operation area reconstructed according to a three-dimensional image before a patient operation.
The POS represents the current virtual space pose where the reconstructed three-dimensional image is located, at the moment, the optimized virtual space pose is matched with the real pose of the physical space where the patient in the real world is located, and the operation planning made by the doctor on the three-dimensional image (or the virtual two-dimensional image corresponding to the three-dimensional image) in the virtual space corresponds to the operation area of the patient in the physical space, so that a foundation is laid for guiding the robot to perform an operation.
In an embodiment of the present application, before S101, the image registration method further includes the following steps:
s201, reconstructing a three-dimensional model of the operation area of the patient according to the preoperative three-dimensional image of the patient.
In one embodiment of the present application, the preoperative three-dimensional image of the patient is a three-dimensional image obtained by an imaging device such as CT, X-ray machine, MRI, etc.
S202, generating two-dimensional images by using a DRR algorithm according to the three-dimensional model.
In an embodiment of the present application, the two-dimensional images generated using the DRR algorithm may be a positive image and a lateral image.
S203, performing operation planning on a two-dimensional image or a three-dimensional model before operation;
s204, acquiring two-dimensional images generated by imaging equipment in operation;
in an embodiment of the present application, the two-dimensional images generated with the imaging apparatus may be a positive-side image and a negative-side image.
The imaging device may be a C-arm X-ray machine or the like.
S205, carrying out similarity measure calculation on a two-dimensional image generated before operation and a two-dimensional image generated in operation; if the similarity measure reaches a preset extremum, the registration is ended, and if the similarity measure does not reach the preset extremum, S101 is performed.
Referring to fig. 2, an image registration apparatus provided in an embodiment of the present application may be a computer program or a program code running in a computer device, for example, the image registration apparatus is an application software; the image registration apparatus may be used to perform the corresponding steps in the image registration method provided in the embodiments of the present application. An image registration apparatus provided in an embodiment of the present application includes:
the acquisition module 11 is configured to acquire a six-dimensional initial pose and a six-dimensional pose variation range of a pre-operative three-dimensional image of a patient when the similarity measure does not reach a preset extremum in the image registration process;
the searching module 12 is used for setting an approximation value, searching each dimension pose and searching for an optimal solution;
and the termination search module 13 is used for carrying out the sum of the DRR projection generated after the translation rotation of the three-dimensional model and the corresponding X-ray image according to the change value of each current dimension and carrying out the similarity calculation, and outputting the pose POS after optimizing the search if the difference value is smaller than the current set value.
The image registration apparatus provided in an embodiment of the present application and the image registration method provided in an embodiment of the present application belong to the same concept, and detailed implementation processes thereof are shown throughout the specification, and are not repeated here.
An embodiment of the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of an image registration method as provided by an embodiment of the present application.
Fig. 3 shows a specific block diagram of a computer device according to an embodiment of the present application, where a computer device 100 includes: one or more processors 101, a memory 102, and one or more computer programs, wherein the processors 101 and the memory 102 are connected by a bus, the one or more computer programs being stored in the memory 102 and configured to be executed by the one or more processors 101, the processor 101 implementing the steps of the image registration method as provided by an embodiment of the present application when the computer programs are executed.
In the application, when the similarity measure does not reach a preset extremum in the image registration process, acquiring a six-dimensional initial pose and a six-dimensional pose change range of a preset three-dimensional image of a patient before operation; setting an approximation value, searching each dimension pose, and searching for an optimal solution; and carrying out the parallel movement and rotation on the three-dimensional model according to the current change value of each dimension to generate a sum of the DRR projection and the corresponding X-ray image after similarity calculation, and if the difference is smaller than the current set value, optimizing search and outputting the pose POS. Therefore, the extremum can be calculated rapidly, the registration time is reduced, and the registration efficiency is improved.
It should be understood that the steps in the embodiments of the present application are not necessarily sequentially performed in the order indicated by the step numbers. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. A method of image registration, the method comprising the steps of:
s101, when similarity measure does not reach a preset extremum in the image registration process, acquiring a six-dimensional initial pose and a six-dimensional pose change range of a preset three-dimensional image of a patient before operation;
s102, setting an approximation value, and searching each dimension pose by adopting a binary search algorithm or a golden section search algorithm to find an optimal solution;
and S103, carrying out similarity calculation on the generated DRR projection and the corresponding X-ray image after carrying out translation rotation on the three-dimensional model according to the change value of each current dimension, and if the difference value is smaller than the current set value, terminating the optimization search and outputting the pose.
2. The method of claim 1, wherein the pose is a current virtual spatial pose in which the reconstructed three-dimensional image is located, and the three-dimensional model is a three-dimensional model of a patient's surgical field reconstructed from a pre-operative three-dimensional image of the patient.
3. The method of claim 1, wherein the six-dimensional initial pose is pos= { TX, TY, TZ, RX, RY, RZ }, POS is a six-dimensional initial pose, TX, TY, and TZ are translation amounts along an X coordinate axis, a Y coordinate axis, and a Z coordinate axis, RX, RY, and RZ are rotation angles around the X coordinate axis, the Y coordinate axis, and the Z coordinate axis, respectively, and the six-dimensional pose change range RNG is rng= { RTX, RTY, RTZ, RRX, RRY, RRZ }, RTX, RTY, RTZ, RRX, RRY, and RRZ are change ranges of TX, TY, TZ, RX, RY and RZ, respectively.
4. A method according to claim 3, wherein S102 comprises the steps of:
s1021, start=pos [ i ] -0.382 x RNG [ i ], end=pos [ i ] +0.618 x RNG [ i ], where POS [ i ] is each dimension pose, RNG [ i ] is a variation range of each dimension pose, RNG [ i ] (i=0, 1,2, … 5), POS [ i ] (i=0, 1,2, … 5);
s1022, step len= (END-START)/1.618, LEFT END left=start+len, RIGHT END right=end-LEN,
let POS [ i ] =LEFT, do DRR projection, calculate the similarity with X-ray image to obtain the first similarity SL,
making POS [ i ] =RIGHT, making DRR projection, calculating similarity with X-ray image to obtain second similarity SR,
end=left if SL > SR, else start=right;
s1023, repeating S1022 until |left-right| < = DELTA returns
Return value ret= (left+right)/2, where DELTA is the approximation;
s1024, calculating the similarity Si between the pose POS [ i ] and the corresponding X-ray image after DRR projection and the similarity Sn between the pose POS [ i ] and the corresponding X-ray image after DRR projection when POS [ i ] =RET, wherein the maximum value of the similarity is Smax; smax=si if Si > Sn, otherwise smax=sn, POS [ i ] =ret.
5. The method of claim 1, wherein prior to S101, the image registration method further comprises the steps of:
s201, reconstructing a three-dimensional model of an operation area of a patient according to a three-dimensional image of the patient before operation;
s202, generating two-dimensional images by using a DRR algorithm according to a three-dimensional model;
s203, performing operation planning on a two-dimensional image or a three-dimensional model before operation;
s204, acquiring two-dimensional images generated by imaging equipment in operation;
s205, carrying out similarity measure calculation on a two-dimensional image generated before operation and a two-dimensional image generated in operation; if the similarity measure reaches a preset extremum, the registration is ended, and if the similarity measure does not reach the preset extremum, S101 is performed.
6. The method of claim 5, wherein the pre-operative three-dimensional image of the patient is a three-dimensional image obtained by a CT device, an X-ray machine, or an MRI device, and the imaging device is a C-arm X-ray machine.
7. The method of claim 5, wherein the two-dimensional images generated using the DRR algorithm are a normal image and a side image; the two-dimensional images generated with the imaging device are a normal image and a side image.
8. An image registration apparatus, the apparatus comprising:
the acquisition module is used for acquiring a six-dimensional initial pose and a six-dimensional pose change range of a pre-operation three-dimensional image of a patient when the similarity measure does not reach a preset extremum in the image registration process;
the search module is used for setting an approximation value, searching each dimension pose by adopting a binary search algorithm or a golden section search algorithm, and searching for an optimal solution;
and the termination search module is used for carrying out the sum of the DRR projection generated after the translation rotation of the three-dimensional model and the corresponding X-ray image according to the change value of each current dimension, carrying out the similarity calculation, and outputting the pose after optimizing the search if the difference value is smaller than the current set value.
9. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the image registration method according to any one of claims 1 to 7.
10. A computer device, comprising:
one or more processors;
a memory; and one or more computer programs, the processor and the memory being connected by a bus, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, characterized in that the processor, when executing the computer programs, implements the steps of the image registration method according to any of claims 1 to 7.
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| CN115068110A (en) * | 2022-06-14 | 2022-09-20 | 中国人民解放军总医院第一医学中心 | Image registration method and system for femoral neck fracture surgery navigation |
| CN115526929A (en) * | 2022-09-28 | 2022-12-27 | 苏州微创畅行机器人有限公司 | Image-based registration method and device |
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| CN115068110A (en) * | 2022-06-14 | 2022-09-20 | 中国人民解放军总医院第一医学中心 | Image registration method and system for femoral neck fracture surgery navigation |
| CN115526929A (en) * | 2022-09-28 | 2022-12-27 | 苏州微创畅行机器人有限公司 | Image-based registration method and device |
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