Route generation method and system for mouse cranial window surgery and storage medium

Abstract

The invention relates to the technical field of surgical equipment, and particularly discloses a path generation method and system for a mouse cranial window surgery and a storage medium, an optical fiber coupler is used for beam splitting to a reflector and focusing on the surface of a mouse cranial bone, light reflected by the reflector and the mouse cranial window surgery interferes in the optical fiber coupler and is output to a spectrograph to capture an interference light signal, and the interference light signal is transmitted to the optical fiber coupler. The high-speed data acquisition card is used for acquiring; the collected data are processed through Python programming to form a chromatography signal, an algorithm is designed according to the chromatography signal to separate the upper surface and the lower surface, and a craniotomy path used for a micron-precision small carving machine to conduct an automatic craniotomy operation is generated.

Description

Technical Field

[0001] The present invention relates to the technical field of surgical equipment, and in particular to a path generation method, system and storage medium for mouse cranial window surgery. Background Art

[0002] Mice are currently widely used in brain science and neuroscience experiments, but whether it is the introduction of neural probes or high-resolution optical imaging of the brain, part of the mouse skull needs to be removed to expose the relevant brain area, which is where the cranial window comes into play. The thickness of the mouse skull ranges from 100 to 400 microns. Currently, craniotomy mainly relies on manual labor, which has problems such as high training time cost, long craniotomy time, and low success rate.

[0003] Existing research on automated mouse craniotomy technology mainly includes foreign impedance feedback craniotomy machines and contact feedback surface modeling craniotomy machines. The former uses the different impedances of the skull and dura mater for feedback control of the cutting depth. It can only drill through the skull to make an implant window and is easily affected by small blood vessels in the skull, resulting in false positives and premature stopping of the cutting. The latter uses a micro-force contact sensor to contact the skull surface, model the upper surface, and then set a consistent cutting depth, iterate the depth, and manually check whether it meets the requirements after each completion. If the thickest and thinnest parts of the cranial window area differ too much, the effect will be unsatisfactory. Whether it is impedance sensing or contact sensing, the speed is very slow due to the existence of feedback.

[0004] There are currently no commercial mouse surgical robots / platforms on the market, and existing studies have been unable to obtain good cranial window path data, making it impossible to perform fast, high-precision, fully-automatic craniotomy. Therefore, how to optimize the existing system to obtain a high-precision cranial window surgical path and execute it quickly and accurately has become a technical problem that needs to be solved urgently.

[0005] Based on the above problems, the present invention proposes a path generation method, system and storage medium for mouse cranial window surgery. Summary of the invention

[0006] The purpose of the present invention is to provide a path generation method, system and storage medium for mouse cranial window surgery, which solves the problem that cranial window surgery is prone to failure in the prior art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The path generation method for mouse cranial window surgery comprises the following steps:

[0009] Step 1: Fix the mouse at the target position and then use optical interferometry to obtain the spectral data of the mouse;

[0010] Step 2: Perform Python data processing on the mouse spectral data obtained in step 1 to obtain the Aline signal of the current point of the skull, that is, the preliminary tomographic data to be saved;

[0011] Step 3: Move the probe to scan the skull area according to the corresponding tomographic data, so that the tomographic data of the entire skull range can be obtained and the data can be saved;

[0012] Step 4: Based on the tomographic data in step 3, a two-dimensional path for craniotomy is selected. If it is an implanted window, the two-dimensional path is a closed loop line connected end to end; if it is a thinned window, the two-dimensional path is a covered surface, and then the corresponding tomographic data is extracted;

[0013] Step 5: Separate the upper and lower surfaces of the tomographic data in step 4 to generate a three-dimensional craniotomy path.

[0014] On the basis of the above technical solution, the present invention also provides the following optional technical solution:

[0015] In the optional solution: the specific operations in step 1 are:

[0016] The ultra-wideband light source emits low-coherence light with a central wavelength of 1550nm, which is split into two beams by a fiber coupler and hits the reflector and lens respectively, and then is focused on the mouse skull by the lens.

[0017] The mouse is fixed in the mouse holder, and the light reflected from the mouse skull and the light reflected from the reflector interfere in the fiber optic coupler;

[0018] The interference light propagates back from the fiber coupler to the spectrometer, and the spectral data is collected and transmitted to the computer by a high-speed linear array camera and a high-speed acquisition card.

[0019] In the optional scheme: Step three is specifically as follows: Control the X-axis motor, Y-axis motor, and Z-axis motor on the engraving machine through computer serial communication to realize the movement of the main axis in the three-dimensional workspace, and then carry the probe to scan the skull area, so that the tomographic data of the entire skull range can be obtained and the data can be saved. The probe is composed of a collimating lens and a focusing lens.

[0020] In the optional solution: Step 5 specifically comprises: using a segmentation algorithm to separate the upper and lower surfaces to generate a three-dimensional craniotomy path for craniotomy;

[0021] The segmentation algorithm includes a threshold segmentation algorithm, which separates the upper surface of the tomographic data extracted in step 4, then performs linear interpolation on some discrete points and undetected points, and then generates a smooth upper surface contour through mean filtering.

[0022] In the optional scheme: the segmentation algorithm includes a semi-automatic segmentation algorithm, which processes the tomography data in step four to generate a visual tomography map, manually marks the corresponding points in the map, and then performs polynomial fitting, and observes the fitted curve in real time in the map until it fully overlaps with the lower surface. When it overlaps, the punctuation is ended, and a smooth lower surface contour is generated using the fitted polynomial.

[0023] A path generation system for mouse cranial window surgery comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0024] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The present invention uses SDOCT imaging technology, which has a micron-level longitudinal resolution and is non-invasive, avoiding problems such as skull deformation caused by contact. It also uses near-infrared light, which is safer than the currently commonly used X-rays and avoids radiation risks.

[0027] 2. The present invention aims to obtain the depth information of the skull by using the tomographic data of OCT before craniotomy, and then pre-set the drilling depth to generate the corresponding craniotomy path to complete cranial window surgeries for different purposes. Compared with the existing impedance sensing automatic craniotomy surgery platform and contact sensing automatic craniotomy surgery platform, the present invention has a higher success rate, higher precision, faster speed and wider application;

[0028] 3. The OCT technology used in the present invention is also a commonly used technology for high-resolution optical imaging. The overall device is easy to use under the interactive design of GUI, and is also conducive to direct on-site observation in conjunction with imaging technology after craniotomy. It has great value and application potential in biological and scientific research. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a principle block diagram of the present invention.

[0030] Figure 2 It is a detailed system diagram of the present invention.

[0031] Figure 3 It is the work flow chart of the present invention.

[0032] Figure 4 Schematic diagram of the segmentation method of the present invention.

[0033] Figure 5It is a schematic diagram of the method for realizing the calibration of the drill bit and scanning point positions according to the present invention.

[0034] Notes on the figure markings: ultra-wideband light source 1, fiber coupler 2-1, reflector 2-2, fiber collimator 2-3, lens 2-4, engraving machine 3-1, mouse holder 3-2, X-axis motor 4-1, Y-axis motor 4-2, Z-axis motor 4-3, high-speed drill 4-4, spectrometer 5-1, high-speed linear array camera 5-2, high-speed acquisition card 5-3, computer 6. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] like Figure 1-Figure 5 As shown, an embodiment of the present invention provides a path generation method for mouse cranial window surgery, comprising:

[0037] In step (1), an ultra-wideband light source 1 emits low-coherence light with a central wavelength of 1550 nm and a bandwidth of 210 nm, which is split into two beams by a fiber coupler 2-1 and hits a reflector 2-2 at a ratio of 25% and 75% respectively, and is collimated by a lens 2-3 and focused on the mouse skull by a lens 2-4;

[0038] Step (2) The mouse is fixed on the mouse holder 3-2, and the light reflected from the mouse skull and the light reflected from the reflector 2-2 interfere with each other in the optical fiber coupler 2-1;

[0039] Step (3) The interference light is transmitted from the fiber coupler 2-1 in reverse to the spectrometer 5-1 (Cobra1600, Wasatch Photonics can be used), and the spectral data is collected and transmitted to the computer 6 by the high-speed linear array camera 5-2 (GL2048R-10A-ENC-STD-210, Sensors Unlimited can be used) and the high-speed acquisition card 5-3 (NI-1433 can be used, which can be triggered by the IO card of model NI-6323). At this time, the spectral data is a function of wavelength and light intensity, including DC quantity and AC interference quantity. The AC interference quantity is the data we need.

[0040] Step (4) Data processing is performed using Python in computer 6. First, the original spectrum is averaged to obtain the spectral DC value. After the DC value is subtracted, only the AC interference value remains in the original spectrum.

[0041] Step (5) The AC interference quantity is a function of wavelength and light intensity, and is non-uniform. By using the corresponding relationship between wavelength and wave number, uniform linear interpolation is performed on it to convert the wavelength space (λ) into the wave number space (k), and the result is a function of wave number and light intensity;

[0042] Step (6) Finally, this function is converted to the time domain through inverse Fourier transform to obtain the function of depth and light intensity, that is, the Aline signal of the current point of the skull, which is also the tomographic data to be saved;

[0043] Step (7) controls the X-axis motor 4-1, the Y-axis motor 4-2, and the Z-axis motor 4-3 on the engraving machine 3-1 through the serial communication of the computer 6 to realize the movement of the main shaft in the three-dimensional workspace, and then takes the probe (composed of a collimating lens 2-3 and a focusing lens 2-4) to perform a large-scale scan of the skull area (such as 10mm*10mm, which can cover the skull craniotomy range), so that the tomographic data of the whole skull range can be obtained and the data can be saved;

[0044] Step (8) in the computer 6, a two-dimensional path of craniotomy is selected from the data saved in step (7). If it is an implanted window (only the edge of the cranial window area needs to be polished), the two-dimensional path is a closed loop line connected end to end; if it is a thinned window (the surface of the cranial window area needs to be thinned), the two-dimensional path is a covered surface, and then the corresponding tomographic data on the two-dimensional path is extracted;

[0045] After step (9), a segmentation algorithm is designed based on the extracted tomographic structure data to separate the upper and lower surfaces, generate a three-dimensional craniotomy path for craniotomy, and guide the engraving machine 3-1 to carry the high-speed drill 4-4 to perform craniotomy.

[0046] Segmentation algorithm scheme:

[0047] ① Design a threshold segmentation algorithm to separate the upper surface from the tomographic data extracted in step (8), then perform linear interpolation on some discrete points and undetected points, and then generate a smooth upper surface contour through mean filtering; then design a semi-automatic segmentation algorithm to separate the lower surface, generate a visual tomographic map by processing the tomographic data, manually mark the corresponding points in the map, and then perform polynomial fitting. It can be observed in real time in the map whether the fitted curve fully overlaps with the lower surface. When it overlaps enough, end the punctuation and use the fitted polynomial to generate a smooth lower surface contour;

[0048] ② Use the semi-automatic segmentation algorithm to segment the upper and lower surfaces as the standard. After segmenting enough data, design a semantic segmentation network for training and learning. Then use the trained semantic segmentation network model to automatically segment the upper and lower surface contours for the tomographic structure data.

[0049] The present embodiment uses the open source Pycharm software equipped with the Python programming environment to process the OCT data, obtain the OCT tomographic data of the whole skull surface, then program to select a point of the surface projection diagram, set the diameter, obtain the two-dimensional plane path range of the craniotomy, then extract all the signals on the path by programming, write the segmentation algorithm according to the segmentation scheme to segment the upper and lower surfaces, then set the step size of the drill bit (0.2mm) according to the drill bit width (0.3mm) used, and discretize the upper and lower surface points of the craniotomy from the continuous upper and lower surfaces as the guide of the three-dimensional craniotomy path. Finally, the high-precision engraving machine is calibrated to the skull surface starting point of the selected two-dimensional path, and the craniotomy operation with a depth gradually deepening from 20% to 90% is performed along the three-dimensional craniotomy path.

[0050] Our robotic platform provides a solution for craniotomy for researchers who are not professionally trained. OCT itself is an imaging technology, which is our exploration of image-guided surgical robots. In addition, the OCT probe is integrated on the main axis, which breaks through the limitation of the small OCT imaging area and greatly increases the degree of freedom. In theory, it can scan and operate on any large area within the platform. Therefore, we believe that this technology has great potential in biomedical research. It can perform three-dimensional imaging of large areas, replace optical probes and drills with extremely small errors, and then customize areas to perform minimally invasive surgery at customized depths.

[0051] Figure 3 The figure shows a schematic diagram of the process scheme of the whole system, and the specific implementation example is:

[0052] (1) Scan a large area of ​​the mouse skull with Cscan to obtain tomographic data of the entire skull surface;

[0053] (2) Select the corresponding two-dimensional path in the GUI (user interface) (taking the circular shape of the glass cranial window as an example);

[0054] (3) extracting the tomographic data on the path and separating the upper and lower surfaces according to the tomographic data to obtain the corresponding three-dimensional path;

[0055] (4) Calibrate the drill bit to the starting point of the surgical path and control the engraving machine to perform craniotomy along the three-dimensional path;

[0056] Segmentation method and calibration method:

[0057] Figure 4 is a schematic diagram of the segmentation method. This is a line of Bscan (OCT image, composed of multiple Alines, which is a cross-sectional view) of the mouse skull.

[0058] Figure 4: For the segmentation of the upper surface, we first use Otsu's method to select an adaptive threshold to binarize the image, then connect some gaps through morphological closing operations, and then use the Canny edge detection algorithm to detect the edges of the black and white image to obtain the edge contour map (upper left). Then, by screening the edges and filtering out irrelevant edges, we obtain the upper surface (upper right). For the segmentation of the lower surface, we manually mark some key points on the image (lower left), use high-order polynomial fitting to fit and interpolate these key points, and obtain the lower surface (lower right). Finally, we have the segmented upper and lower surfaces (right).

[0059] Figure 5 Schematic diagram of the method for calibrating the drill and scan point positions in order to achieve tool switching from imaging to actual surgery.

[0060] Figure 5 :Our optical path and the drill bit are not in the same path, so we need to calibrate them so that we can successfully calibrate the drill bit to the starting point after imaging and segmentation. The lateral distance [Δx, Δy] between the scanning point and the end of the drill bit is fixed, and the longitudinal distance has the following relationship:

[0061] Δz=Δz1+Δz2 (1)

[0062] Where Δz is the total longitudinal distance, Δz1 is the distance between the end of the drill bit and the imaging zero point, which is fixed. Δz2 is the actual distance from the imaging zero point to the upper surface, which is obtained by the following formula:

[0063] Δz2=k air ·z up (2)

[0064] In the formula, z up is the pixel distance from the upper surface to the imaging zero point, k air It is the pixel resolution in the air. Since the previous OCT data processing is uniformly interpolated, the pixel resolution here is a constant. In this embodiment, the pixel resolution of the OCT signal in the air is 5μm, and the upper surface is located at the 100 pixel point, so Δz2=100*5=500μm.

[0065] Thus, the relative distance between the drill bit and the scanning point can be obtained, and the replacement of the OCT probe and the drill bit can be realized, which is also a key step in the transition from imaging to craniotomy.

[0066] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A path generation method for mouse cranial window surgery, characterized in that: The following steps are involved: Step 1: Fix the mouse at the target position and then use optical interferometry to obtain the spectral data of the mouse; Step 2: Perform Python data processing on the mouse spectral data obtained in step 1 to obtain the Aline signal of the current point of the skull, that is, the preliminary tomographic data to be saved; Step 3: Move the probe to scan the skull area according to the corresponding tomographic data, so that the tomographic data of the entire skull range can be obtained and the data can be saved; Step 4: Based on the tomographic data in step 3, a two-dimensional path for craniotomy is selected. If it is an implant window, the two-dimensional path is a closed loop line connected end to end; If it is a thinned window, the two-dimensional path is a coverage surface, and then the corresponding tomographic data are extracted; Step 5: Separate the upper and lower surfaces of the tomographic data in step 4 to generate a three-dimensional craniotomy path.

2. The path generation method for mouse cranial window surgery according to claim 1, characterized in that: The specific operations in step one are: An ultra-wideband light source (1) emits low-coherence light with a central wavelength of 1550 nm, which is split into two beams by a fiber coupler (2-1), and then hits a reflector (2-2) and is collimated by a lens (2-3) and then focused on a mouse skull by a lens (2-4); The mouse is fixed on the mouse holder (3-2), and the light reflected from the mouse skull and the light reflected from the reflector (2-2) interfere with each other in the optical fiber coupler (2-1); The interference light propagates back from the optical fiber coupler (2-1) to the spectrometer (5-1), and the spectrum data is collected and transmitted to the computer (6) by the high-speed linear array camera (5-2) and the high-speed acquisition card (5-3).

3. The path generation method for mouse cranial window surgery according to claim 2, characterized in that: Step three is specifically as follows: the X-axis motor (4-1), the Y-axis motor (4-2), and the Z-axis motor (4-3) on the engraving machine (3-1) are controlled through the serial communication of the computer (6) to realize the movement of the main axis in the three-dimensional workspace, and then the skull area is scanned with the probe, so that the tomographic data of the entire skull range can be obtained and the data can be saved.

4. The path generation method for mouse cranial window surgery according to claim 1, characterized in that: Step 5 is specifically as follows: using a segmentation algorithm to separate the upper and lower surfaces to generate a three-dimensional craniotomy path for craniotomy; The segmentation algorithm includes a threshold segmentation algorithm, which separates the upper surface of the tomographic data extracted in step 4, then performs linear interpolation on some discrete points and undetected points, and then generates a smooth upper surface contour through mean filtering.

5. The path generation method for mouse cranial window surgery according to claim 4, characterized in that: The segmentation algorithm includes a semi-automatic segmentation algorithm, which processes the tomography data in step 4 to generate a visible tomography map, manually marks the corresponding points in the map, and then performs polynomial fitting, and observes the fitted curve in real time in the map until it fully overlaps with the lower surface. When it overlaps, the punctuation is ended, and a smooth lower surface contour is generated using the fitted polynomial.

6. A path generation system for mouse cranial window surgery, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method steps described in any one of claims 1 to 5 are implemented.

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