Automatic biological sample tissue morphology measuring system

Abstract

The invention relates to the field of morphology measurement, in particular to an automatic biological sample tissue morphology measurement system. The system comprises a controller, a tissue tray, a reflective photoelectric sensor and an image acquisition device. The controller executes the following steps: carrying out image segmentation processing on image information of a to-be-measured sample; and obtaining an amplitude threshold value corresponding to the current scanning position according to the transparency coefficient corresponding to the independent image segmentation area to which the mapping position belongs, and determining the receiving time of the first signal amplitude value greater than or equal to the amplitude threshold value as the signal receiving time. According to the corresponding relation between the surfaces of the areas with different transparency and the amplitudes of the reflection signals, the amplitude threshold value corresponding to the real reflection signals better fitting the surface of the current position is obtained. On the basis that the generation time of the surface signal is earlier than the generation time of the internal structure signal, the accuracy of the signal receiving time is further improved, and the precision of the biological tissue sample thickness information obtained through final calculation is improved.

Description

Technical Field

[0001] The present invention relates to the field of topography measurement, and particularly to an automatic biological sample tissue topography measurement system. Background Art

[0002] The topography size and weight of biological tissue samples are important research contents in medical, biological and other research work. The size and weight data of biological samples are usually measured by using a ruler and an electronic scale. For relatively small biological tissues, such as mouse uterine implantation sites, small-volume tumors, etc., there are large subjectivity and measurement errors, which directly affect biological results, bringing great inconvenience to basic experiments and clinical sample collection, and manual measurement is time-consuming and laborious with heavy workload.

[0003] In the prior art, a reflective photoelectric sensor is used to emit a measurement signal to a biological tissue sample, and the thickness of the biological tissue sample is calculated by the time difference between the signal emission time and the signal reception time. Usually, the time corresponding to the signal with the largest amplitude in the received return signal waveform is used as the signal reception time. However, in some biological tissue samples, there may be a certain semi-transparent area or even the whole is in a semi-transparent state. Thus, in the semi-transparent area, not only its surface will reflect the signal, but also its interior and even the bottom tray will reflect the signal. And due to different reflectivities and the superposition of light, the amplitude of the reflected signal of the internal structure of the semi-transparent area in the received return signal waveform may be greater than the amplitude of the reflected signal of the surface structure of the semi-transparent area, which will lead to an error in the determined signal reception time and ultimately reduce the accuracy of the thickness information of the biological tissue sample. Summary of the Invention

[0004] For one of the above technical problems, the technical solution adopted by the present invention is as follows: According to one aspect of the present invention, there is provided an automatic biological sample tissue topography measurement system, the system comprising: a controller, a tissue tray, a reflective photoelectric sensor and an image acquisition device. The photoelectric sensor is arranged above the tissue tray. The tissue tray is a self-luminous tissue tray. Both the photoelectric sensor and the image acquisition device are communicatively connected to the controller.

[0005] The controller is configured to perform the following steps: According to the transparency information of the image, perform image segmentation processing on the image information of the sample to be measured collected by the image acquisition device, and generate segmentation information corresponding to the image information. The segmentation information includes multiple independent image unit information. The independent image unit information includes the contour information and the transparency coefficient of the independent image segmentation region. The transparency coefficient is positively correlated with the transparency.

[0006] If the mapped position of the current scanning position of the photoelectric sensor in the image information is located in an independent image segmentation region, then according to the transparency coefficient corresponding to the independent image segmentation region to which the mapped position belongs, obtain the amplitude threshold corresponding to the current scanning position from the preset mapping table. The transparency coefficient and the amplitude threshold in the preset mapping table are negatively correlated.

[0007] Determine the signal reception time of the current scanning position as the reception time of the signal amplitude that is the first to be greater than or equal to the amplitude threshold in the reflected signal waveform collected by the photoelectric sensor at the current scanning position.

[0008] Furthermore, the controller is further configured to perform the following steps: Generate the thickness information d corresponding to the current scanning position of the sample to be measured according to the signal emission time t1, the signal reception time t2 corresponding to the current scanning position, and the distance L between the photoelectric sensor and the tissue tray.

[0009] d satisfies the following steps: .

[0010] Where C is the speed of light.

[0011] Furthermore, the controller is further configured to perform the following steps: Generate a three-dimensional contour model of the sample to be measured according to the thickness information corresponding to all scanning positions in the sample to be measured.

[0012] Furthermore, the segmentation information includes multiple boundary image unit information. The boundary image unit information includes the contour information of the boundary image segmentation region, the first transparency coefficient, and the second transparency coefficient. The boundary image segmentation region includes an inward contraction region that contracts inward along the original segmentation boundary of the independent image segmentation region and an outward expansion region that expands outward along the original segmentation boundary of the independent image segmentation region. The first transparency coefficient is the transparency coefficient corresponding to the independent image segmentation region to which the inward contraction region belongs. The second transparency coefficient is the transparency coefficient corresponding to the independent image segmentation region to which the outward expansion region belongs.

[0013] The controller is further configured to perform the following steps: If the mapped position of the current scanning position of the photoelectric sensor in the image information is located in the boundary image segmentation region, then generate the first predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation region to which the inward contraction region belongs 1 . Generate the second predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation region to which the outward expansion region belongs 2 .

[0014] According to Alpha 1 and Alpha 2, generate the reference transparency coefficient Alpha corresponding to the current scanning position c Alpha c satisfies the following conditions: Alpha c = (Q2 × Alpha 1 + Q1 × Alpha 2 ) / h.

[0015] Where h is the width of the boundary image segmentation area, Q1 is the distance from the mapping position in the image information at the current scanning position to the boundary of the adduction area, and Q2 is the distance from the mapping position in the image information at the current scanning position to the boundary of the expansion area.

[0016] Take the transparency coefficient closest to Alpha among the first transparency coefficient and the second transparency coefficient as the transparency coefficient corresponding to the current scanning position. c

[0017] Further, after taking the transparency coefficient closest to Alpha among the first transparency coefficient and the second transparency coefficient as the transparency coefficient corresponding to the current scanning position, the controller is further configured to perform the following steps: c According to the transparency coefficient corresponding to the current scanning position, obtain the amplitude threshold corresponding to the current scanning position from the preset mapping table.

[0018] Determine the reception time of the signal amplitude of the first signal greater than or equal to the amplitude threshold in the reflected signal waveform collected by the photoelectric sensor at the current scanning position as the signal reception time at the current scanning position.

[0019] Further, generate the first predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation area to which the adduction area belongs 1 , including: Use the inpaint function to generate the first predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation area to which the adduction area belongs 1

[0020] Further, generate the first predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation area to which the adduction area belongs 1 , including: Use the interpolation method to generate the first predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation area to which the adduction area belongs 1

[0021] ​​​​Further, the image acquisition device includes: an RGB camera. The image information includes RGB images.

[0022] According to the transparency information of the image, perform image segmentation processing on the image information of the sample to be measured collected by the image acquisition device, and generate segmentation information corresponding to the image information, including: Input the RGB image of the sample to be measured obtained by the RGB camera into the target U-Net model to generate segmentation information corresponding to the image information.

[0023] Further, it further includes: a lateral movement component and a longitudinal movement component.

[0024] The longitudinal movement component is arranged above the lateral movement component, and the movement directions of the longitudinal movement component and the lateral movement component are perpendicular to each other.

[0025] The tissue tray is arranged on the lateral movement component, and the photoelectric sensor is arranged on the longitudinal movement component.

[0026] The lateral movement component includes: a first slider group, a second slider group, a lateral lead screw, a lead screw motor, and a tissue tray base.

[0027] The tissue tray base is arranged on the sliding parts of the first slider group and the second slider group. The driving part of the lateral lead screw is fixedly connected to the tissue tray base. The lead screw motor is fixedly connected to the lateral lead screw. The tissue tray base is used to place the tissue tray.

[0028] Further, the longitudinal movement component includes: a longitudinal lead screw, a lead screw motor, and an optical scanning measurement mechanism bracket; The lead screw motor is connected to the longitudinal lead screw, and the optical scanning measurement mechanism bracket is connected to the sliding part of the lead screw. The photoelectric sensor is arranged on the optical scanning measurement mechanism bracket.

[0029] The present invention has at least one of the following beneficial effects: In the present invention, first, through image segmentation technology, the image of the sample to be measured (such as biological tissue) collected is segmented according to different transparencies, so that regions with different transparency levels in the tissue can be divided. Then, according to the corresponding relationship between the surface of regions with different transparency levels and the amplitude of the reflection signal, a preset mapping table is established. Then, according to the segmentation region to which the mapping position of the current scanning position in the image belongs, the amplitude threshold corresponding to the true reflection signal that is more suitable for the current position surface is obtained from the mapping table. Furthermore, it can provide a favorable reference for screening the received signal, and the waveform of the reflection signal that may be the target received signal in the reflection signal waveform is screened through this amplitude threshold to ensure that the true reflection signal on the surface is screened out.

[0030] Meanwhile, since the distance between the surface of the sample to be measured and the optoelectronic sensor is less than the distance between the internal structure of the sample to be measured and the optoelectronic sensor, in the reflected signal waveform, the generation time of the signal corresponding to the surface of the sample to be measured is earlier than the generation time of the signal corresponding to the internal structure of the sample to be measured. Thus, among the selected signals, the reception time of the signal amplitude of the first signal greater than or equal to the amplitude threshold is determined as the signal reception time at the current scanning position, so as to further improve the accuracy of the signal reception time and the precision of the thickness information of the biological tissue sample finally calculated. Brief Description of the Drawings

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0032] Figure 1 It is a schematic diagram of the overall structure of the automatic biological sample tissue topography measurement system provided by the embodiment of the present invention; Figure 2 It is a schematic diagram of the exploded structure of the automatic biological sample tissue topography measurement system provided by the embodiment of the present invention; Figure 3 It is a schematic diagram of the exploded structure of the base structure provided by the embodiment of the present invention; Figure 4 It is a schematic diagram of the exploded structure of the lateral movement component provided by the embodiment of the present invention; Figure 5 It is a schematic diagram of the exploded structure of the longitudinal movement component provided by the embodiment of the present invention; Figure 6 It is a schematic diagram of the flow of the automatic biological sample tissue topography measurement method provided by the embodiment of the present invention; Figure 7 It is a schematic diagram of the biological sample tissue thickness calculation principle provided by the embodiment of the present invention; Figure 8 It is a schematic diagram of the region after RGB image segmentation provided by the embodiment of the present invention.

[0033] Reference Signs 1. Base; 101. Protective cover for scanning and measuring mechanism; 102. First support rod; 103. Second support rod; 104. Second support protective cover; 105. Installation base; 106. First support protective cover; 107. Third support rod; 108. Fourth support rod; 2. Lateral movement component; 201. First slider group; 202. Lateral lead screw mounting base; 203. Lateral lead screw; 204. First mounting claw; 205. Second mounting claw; 206. Tissue tray base; 207. Tissue tray; 208. Third mounting claw; 209. Lateral lead screw nut; 210. Fourth mounting claw; 211. Lateral lead screw motor; 212. Lateral lead screw motor mounting base; 213. Second slider group; 3. Longitudinal movement component; 301. Longitudinal lead screw mounting base; 302. Longitudinal lead screw; 303. Longitudinal movement component bracket; 304. Photoelectric sensor; 305. Sensor mounting plate; 306. Longitudinal lead screw motor; 307. Longitudinal lead screw motor mounting base; 308. Longitudinal nut guide rail group; 309. Longitudinal nut fitting; 310. Longitudinal nut; 40. Independent image segmentation area; 41. Boundary image segmentation area; 42. Original segmentation boundary. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the protection scope of the present invention.

[0035] As a possible embodiment of the present invention, as Figures 1 to 5 shown, an automatic biological sample tissue morphology measurement system is provided. The system includes: a controller, a tissue tray 207, a reflective photoelectric sensor, and an image acquisition device. The photoelectric sensor 304 is arranged above the tissue tray 207. The tissue tray 207 is a self-luminous tissue tray. Both the photoelectric sensor 304 and the image acquisition device are communicatively connected to the controller.

[0036] This measurement system further includes an installation and driving component for the tissue tray 207 and the photoelectric sensor 304, which is used to drive the photoelectric sensor 304 along a preset scanning path to scan and measure the sample to be measured (i.e., the biological sample tissue) placed on the tissue tray 207. Specifically, the installation and driving component includes the following specific structures: A base 1, a lateral movement component 2, and a longitudinal movement component 3. The lateral movement component 2 is installed on the base 1 and can move horizontally left and right. The longitudinal movement component 3 is suspended and installed on the base 1 and can move longitudinally forward and backward, thereby driving the photoelectric sensor to measure the longitudinal and lateral dimensions and shapes of the sample tissue and generating a three-dimensional model.

[0037] As shown in FIG. 3, the base 1 includes a protective cover 101 for the scanning and measuring mechanism, a first support rod 102, a second support rod 103, a second support protective cover 104, a mounting base 105, a first support protective cover 106, a third support rod 107, and a fourth support rod 108.

[0038] The protective cover 101 for the scanning and measuring mechanism is a square protective cover, which is installed outside the longitudinal moving assembly 3 and is fixedly connected to the first support rod 102, the second support rod 103, the third support rod 107, and the fourth support rod 108 by nuts.

[0039] The first support rod 102, the second support rod 103, the third support rod 107, and the fourth support rod 108 are threaded rods with smooth shafts in the middle at both ends. The lower ends are threadedly installed on the mounting base 105, and the upper ends are threaded for fixedly supporting the longitudinal moving assembly 3 and the protective cover 101 of the scanning and measuring mechanism.

[0040] The second support protective cover 104 and the first support protective cover 106 are installed on the mounting base 105 to assist the first support rod 102, the second support rod 103, the third support rod 107, and the fourth support rod 108 in fixedly supporting the longitudinal moving assembly 3 and the protective cover 101 of the scanning and measuring mechanism.

[0041] The mounting base 105 has threaded holes, which are installed in a matching manner with the first support rod 102, the second support rod 103, the third support rod 107, and the fourth support rod 108 and are used for fixing the transverse moving assembly 2.

[0042] As Figure 4 shown, the transverse moving assembly 2 includes a first slider group 201, a transverse lead screw mounting base 202, a transverse lead screw 203, a first mounting claw 204, a second mounting claw 205, a tissue tray base 206, a tissue tray 207, a third mounting claw 208, a transverse lead screw nut 209, a fourth mounting claw 210, a transverse lead screw motor 211, a transverse lead screw motor mounting base 212, and a second slider group 213.

[0043] The first slider group 201 and the second slider group 213 are slider guide rail groups, which guide the movement of the transverse lead screw 203 and the transverse lead screw nut 209. They are installed on the mounting base 105 and are installed in cooperation with the threaded holes on the mounting base 105.

[0044] The transverse lead screw mounting base 202 is installed on the mounting base 105 and is a bearing support seat, which is installed in cooperation with one end of the transverse lead screw 203.

[0045] The horizontal lead screw 203 is a ball screw, one end of which is installed on the horizontal lead screw mounting base 202, and the other end is connected to the horizontal lead screw motor 211. The horizontal lead screw nut 209 is a ball screw nut, which is installed in cooperation with the horizontal lead screw 203. When the horizontal lead screw 203 rotates, it drives the horizontal lead screw nut 209 to move left and right. The upper surface is fixedly connected to the tissue tray base 206 by screws, driving the tissue tray base 206 to move.

[0046] The first mounting claw 204, the second mounting claw 205, the third mounting claw 208, and the fourth mounting claw 210 are sample fixing claws on the tissue tray 207, made of colorless transparent material.

[0047] The tissue tray 207 is square. The lower surface can be fixed to the weight sensor for weighing. A positioning groove is opened 5 mm away from the edge on the upper surface, serving as the starting position for the cyclic movement of the longitudinal movement assembly 3. The tissue sample is placed or fixed on the tissue tray 207.

[0048] The horizontal lead screw motor 211 is a permanent magnet synchronous motor with high precision. It is the power source for horizontally moving the tissue tray 207 and is fixed on the horizontal lead screw motor mounting base 212.

[0049] The horizontal lead screw motor mounting base 212 is used to install the horizontal lead screw motor 211 and is installed in cooperation with the threaded hole on the mounting base 105.

[0050] As Figure 5 shown, the longitudinal movement assembly 3 includes a longitudinal lead screw mounting base 301, a longitudinal lead screw 302, a longitudinal movement assembly bracket 303, a photoelectric sensor 304, a sensor mounting plate 305, a longitudinal lead screw motor 306, a longitudinal lead screw motor mounting base 307, a longitudinal nut guide rail group 308, a longitudinal nut fitting 309, and a longitudinal nut 310.

[0051] The longitudinal lead screw mounting base 301 is installed on the longitudinal movement assembly bracket 303, which is a bearing support seat and is installed in cooperation with one end of the longitudinal lead screw 302.

[0052] The longitudinal lead screw 302 is a ball screw, one end of which is installed on the longitudinal lead screw mounting base 301, and the other end is connected to the longitudinal lead screw motor 306.

[0053] The longitudinal movement assembly bracket 303 is the mounting base of the longitudinal movement assembly 3. It is fixedly connected to the first support rod 102, the second support rod 103, the third support rod 107, the fourth support rod 108, and the scanning and measuring mechanism protective cover 101 by nuts. There are threaded holes on it, which are convenient for installation in cooperation with the longitudinal lead screw motor mounting base 307 and the longitudinal nut guide rail group 308.

[0054] The photoelectric sensor 304 is installed on the sensor mounting plate 305 and is a sensor for identifying the contour of the tissue sample, collecting the morphological information of the sample to be measured. The photoelectric sensor 304 in this embodiment is a reflective photoelectric sensor, which determines the presence, distance or surface characteristics of an object by emitting a light beam and detecting the reflected light, specifically determining the presence, distance or surface characteristics of an object according to the intensity of the reflected light.

[0055] The sensor mounting plate 305 is provided with holes and is cooperatively installed with the longitudinal nut guide rail group 308 and the photoelectric sensor 304, and moves longitudinally back and forth with the slider of the longitudinal nut guide rail group 308, driving the photoelectric sensor 304 to collect contour information.

[0056] The longitudinal lead screw motor 306 is a permanent magnet synchronous motor with high precision and is the power source of the longitudinal moving assembly 3, and is fixed on the longitudinal lead screw motor mounting seat 307.

[0057] The longitudinal lead screw motor mounting seat 307 is used for installing the longitudinal lead screw motor 306 and is cooperatively installed with the threaded hole on the longitudinal moving assembly bracket 303.

[0058] The longitudinal nut guide rail group 308 guides the movement of the longitudinal nut 310 and the longitudinal lead screw 302, is installed on the longitudinal moving assembly bracket 303, and is cooperatively installed with the threaded hole on the longitudinal moving assembly bracket 303.

[0059] The longitudinal nut fitting 309 is fixed to the longitudinal nut 310 and is also fixed to the sensor mounting plate 305 by screws, driving the photoelectric sensor 304 and the sensor mounting plate 305 to move.

[0060] The longitudinal nut 310 is a ball screw nut, is cooperatively installed with the longitudinal lead screw 302, is fixed to the longitudinal nut fitting 309, and rotates with the longitudinal lead screw 302 to drive the longitudinal nut 310 to move longitudinally back and forth, thereby driving the longitudinal nut fitting 309, the sensor mounting plate 305, and the photoelectric sensor 304 to move longitudinally back and forth.

[0061] Specifically, the scanning method of the morphology measurement system in this embodiment includes the following steps: S1: Place the tissue sample to be measured in the tissue tray 207 in the transverse moving assembly 2, flatten and unfold it, and turn to step S2; S2: Start the device. The photoelectric sensor 304 determines the initial position according to the right edge positioning groove and the front positioning groove of the tissue tray 207, and turn to step S3; The sampling frequency of the photoelectric sensor 304 can be 10 Hz, and can be specifically adjusted according to the modeling accuracy.

[0062] S3: Wait for the photoelectric sensor 304 to reach the initial position, start scanning the tissue sample to be measured, and turn to step S4; S4: Move the transverse movement component 2 horizontally from left to right at a speed of 1 cm / s, keep the photoelectric sensor 304 stationary, and go to step S5; S5: Determine the output signal of the photoelectric sensor 304. If it reaches the right edge positioning groove, go to step S6; otherwise, continue step S4; S6: Stop the movement of the transverse movement component 2 and keep it stationary. The photoelectric sensor 304 moves longitudinally from front to back in steps of 0.05 cm, and go to step S7; S7: Determine the output signal of the photoelectric sensor 304. If it reaches the rear edge positioning groove, go to step S8; otherwise, continue step S4, but at this time, the moving direction of the transverse movement component 2 is opposite to the previous moving direction, thereby forming a serpentine scanning path.

[0063] S8: Scanning ends.

[0064] The automatic biological sample tissue morphology measurement system provided in this embodiment fully considers the morphological characteristics of biological samples, adopts a high-precision photoelectric sensor, can complete the measurement of the tissue morphology of semi-transparent and opaque biological samples, and has a high recognition accuracy for small biological tissues, reducing the previous manual recognition error, improving the accuracy of data in biological experiments and clinical sample collection, and reducing the burden on experimental personnel. On the basis of morphological recognition, the output signal is recognized by the host computer to complete the tissue morphology modeling of biological samples, which is convenient for recording and retention. The usage method of the present invention is simple and can meet the requirements of different samples.

[0065] In addition, the following solution is also provided in this embodiment, as Figure 6 shown. This solution is used to obtain the thickness information of the biological sample tissue at each scanning position for subsequent 3D modeling. Specifically, this method is executed by the controller and includes the following steps: S100: According to the transparency information of the image, perform image segmentation processing on the image information of the sample to be measured (i.e., biological sample tissue) collected by the image acquisition device to generate segmentation information corresponding to the image information. The segmentation information includes multiple independent image unit information. The independent image unit information includes the contour information and transparency coefficient of the independent image segmentation region 40. The transparency coefficient is positively correlated with the transparency.

[0066] Specifically, the image acquisition device includes: an RGB camera, and the image information includes an RGB image.

[0067] S100 includes: S101: Input the RGB image of the sample to be measured obtained by the RGB camera into the target U-Net model to generate segmentation information corresponding to the image information.

[0068] In this embodiment, since the tissue tray 207 is a self-luminous tray, if there are different regions of the biological sample tissue with a semi-transparent tissue structure, due to the different degrees of transparency, the brightness of the light transmitted through different regions will inevitably be different. Furthermore, when taking pictures with an RGB camera, the boundaries of different semi-transparent regions can be more clearly represented. This facilitates the target U-Net model to more accurately divide different semi-transparent regions in the picture.

[0069] When training the U-Net model, a large number of pictures of different biological sample tissues can be obtained by taking pictures and then manually annotated to form a training set. In addition, according to needs, the input features can be changed to a combined image composed of an RGB image and the corresponding grayscale image and input into the target U-Net model together to further improve the segmentation accuracy of the model.

[0070] In addition, the image acquisition device in this embodiment further includes a polarization camera. The characteristics of the transparent material (such as refractive index, surface roughness, internal scattering) will affect the polarization state of light. The polarization camera can capture the polarization information of light. Thus, according to the high or low degree of polarization (DoP) in the reflected light, different transparency regions in the tissue can be marked. Then, combined with the RGB image obtained by the RGB camera, transparency information can be generated more accurately. Therefore, the target U-Net model can perform more accurate segmentation processing according to the transparency information on the image. In addition, when obtaining training sample data, the transparency of different parts can also be identified by existing optoelectronic sensors, and then the annotation of the transparency coefficient can be generated to facilitate the rapid acquisition of a large number of training samples.

[0071] As Figure 8 shown, after the segmentation processing of this step, the image regions corresponding to different transparency regions in the image will be separated. However, since the transparency and texture material of the biological sample tissue are usually slowly changing at the position of the segmentation boundary, generally the transparency near the segmentation boundary is between the two segmentation regions. Based on this, the present invention needs to perform special processing on the region of the segmentation boundary. Specifically, in the present invention, it is further widened respectively inward and outward along the segmentation boundary to form a boundary image segmentation region 41, which includes the transparency transition region near the segmentation boundary. Thus, after the final segmentation in the present invention, there are two types of segmentation regions in total. One is an independent image segmentation region 40 with relatively uniform transparency, and the other is a boundary image segmentation region 41 with relatively gradual transparency change. The methods for obtaining the thickness of the sample tissue in the above two regions are different, and the specific description is as follows.

[0072] S200: If the mapped position of the current scanning position of the photoelectric sensor in the image information is located in the independent image segmentation region 40, then according to the transparency coefficient corresponding to the independent image segmentation region 40 to which the mapped position belongs, obtain the amplitude threshold corresponding to the current scanning position from the preset mapping table. The transparency coefficient and the amplitude threshold in the preset mapping table are negatively correlated.

[0073] Specifically, in this embodiment, the transparency coefficient corresponding to the independent image segmentation region 40 may be the average transparency of all pixel points included in the independent image segmentation region 40.

[0074] The preset mapping table in this step is for the transparency coefficient and the amplitude threshold of the received signal. The specific corresponding relationship can be set according to the actual usage situation. Different degrees of transparency correspond to different amplitude thresholds, which can then provide a favorable reference when screening the received signals, so as to screen the waveforms in the reflected signal waveform that may be the target received signals through this amplitude threshold to ensure that the true reflected signals on the surface are screened out. Preferably, the amplitude threshold can be set slightly smaller than the amplitude of the true received signal. Thus, it can not only ensure as much as possible that the true received signals are not missed, but also filter out the influence of most environmental noises and reduce the amount of data for subsequent processing.

[0075] S300: Determine the signal reception time of the current scanning position as the reception time of the signal amplitude that is the first to be greater than or equal to the amplitude threshold in the reflected signal waveform collected by the photoelectric sensor at the current scanning position.

[0076] Since the distance between the surface of the biological sample tissue and the photoelectric sensor is less than the distance between the internal structure of the biological sample tissue and the photoelectric sensor, in the reflected signal waveform, the generation time of the signal corresponding to the surface of the biological sample tissue is earlier than the generation time of the signal corresponding to the internal structure of the biological sample tissue. Therefore, among the screened signals, the reception time of the signal amplitude that is the first to be greater than or equal to the amplitude threshold is determined as the signal reception time of the current scanning position, so as to further improve the accuracy of the signal reception time and the accuracy of the thickness information of the biological tissue sample finally calculated.

[0077] S400: Generate the thickness information d corresponding to the current scanning position of the sample to be measured according to the signal emission time t1, the signal reception time t2 corresponding to the current scanning position, and the distance L between the photoelectric sensor and the tissue tray.

[0078] As Figure 7 shown, d satisfies the following steps: .

[0079] Where C is the speed of light.

[0080] S500: Generate a three-dimensional contour model of the sample to be measured based on the thickness information corresponding to all scanning positions in the sample to be measured.

[0081] Through the above calculations, the thickness information corresponding to each scanning position point can be obtained. After the photoelectric sensor 304 completes scanning, a large number of contour points on the surface of the biological sample tissue can be obtained, and a three-dimensional contour model of the biological sample tissue can be formed based on these points.

[0082] As another possible embodiment of the present invention, the segmentation information includes multiple boundary image unit information. The boundary image unit information includes the contour information of the boundary image segmentation region 41, the first transparency coefficient, and the second transparency coefficient. The boundary image segmentation region 41 includes an inwardly contracted region that contracts inward along the original segmentation boundary 42 of the independent image segmentation region 40 and an outwardly expanded region that expands outward along the original segmentation boundary 42 of the independent image segmentation region 40. The first transparency coefficient is the transparency coefficient corresponding to the independent image segmentation region 40 to which the inwardly contracted region belongs. The second transparency coefficient is the transparency coefficient corresponding to the independent image segmentation region 40 to which the outwardly expanded region belongs. Specifically, the outwardly expanded region and the inwardly contracted region can be annular regions formed by expanding outward or inward along the original segmentation boundary 42 by several (such as 3) pixels.

[0083] The controller is further configured to perform the following steps: S600: If the mapped position of the current scanning position of the photoelectric sensor in the image information is located in the boundary image segmentation region 41, generate a first predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation region 40 to which the inwardly contracted region belongs 1 Generate a second predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation region 40 to which the outwardly expanded region belongs. 2 .

[0084] In this step, since the transparency of the boundary image segmentation region 41 transitions from two adjacent independent image segmentation regions 40, the transparency coefficient at the current scanning position can be inferred based on the image information in the two independent image segmentation regions 40.

[0085] In this embodiment, Alpha 1 and Alpha 2 can be obtained through the following method. Since the methods for obtaining Alpha 1 and Alpha 2 are the same, only the method for obtaining Alpha 1 will be exemplified.

[0086] First Use the inpaint function to generate the first predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation region 40 to which the adducted region belongs 1 .

[0087] The inpaint function is an image inpainting algorithm in OpenCV. It can repair unknown regions based on the information of known regions. Therefore, the transparency coefficient at the current scanning position can be generated according to the known transparency information in the image of the independent image segmentation region 40. Moreover, this function is more suitable for regions with similar textures and structures, which exactly matches the imaging characteristics of biological sample tissues in the boundary image segmentation region 41 in this usage scenario.

[0088] Second Use the interpolation method to generate the first predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation region 40 to which the adducted region belongs 1 .

[0089] Similarly, since the transparency of the boundary image segmentation region 41 transitions from two adjacent independent image segmentation regions 40, the transparency of the boundary image segmentation region 41 should smoothly change from the transparencies of the two independent image segmentation regions 40. Therefore, the transparency value of the unknown region is predicted using the transparency values of the known regions through an interpolation algorithm (such as bilinear interpolation, bicubic interpolation).

[0090] S700: According to Alpha 1 and Alpha 2 , generate the reference transparency coefficient Alpha corresponding to the current scanning position c . Alpha c satisfies the following conditions: Alpha c = (Q2 × Alpha 1 + Q1 × Alpha 2 ) / h.

[0091] Where h is the width of the boundary image segmentation region 41, Q1 is the distance from the mapping position in the image information of the current scanning position to the boundary of the adducted region, and Q2 is the distance from the mapping position in the image information of the current scanning position to the boundary of the expanded region.

[0092] Generally, for the current scanning position in the boundary image segmentation region 41, there will also be cases where it is closer to the independent image segmentation region 40 on the adducted region side or closer to the independent image segmentation region 40 on the expanded region side. And generally, the closer to the independent image segmentation region 40, the higher the accuracy of the predicted transparency information. Therefore, in this embodiment, Alpha is formed by distance1 and Alpha 2 as its weight value, and take Q2 / h as Alpha 1 as its weight value, and take Q1 / h as Alpha 2 as its weight value. Thus, if the mapped position in the image information at the current scanning position is closer to the independent image segmentation region 40 on the adduction region side, then Q2 / h > Q1 / h. As a result, when generating Alpha c , Alpha 1 will occupy a greater weight proportion so that Alpha c will be closer to the Alpha generated based on the image information of the independent image segmentation region 40 to which the adduction region belongs 1 .

[0093] S800: Take the transparency coefficient closest to Alpha among the first transparency coefficient and the second transparency coefficient as the transparency coefficient corresponding to the current scanning position. c

[0094] In this embodiment, by comparing the first transparency coefficient and the second transparency coefficient with the predicted Alpha c , the transparency coefficient corresponding to the current transition position can be obtained more accurately. Furthermore, the corresponding amplitude threshold can be obtained more accurately from the mapping table, and finally the signal reception time of the current scanning position can be determined more accurately.

[0095] In addition, in this embodiment, a more fine-grained correspondence between the transparency coefficient and the amplitude threshold can be adaptively set in the preset mapping table. Furthermore, after obtaining Alpha c in S700, the transparency coefficient corresponding to the current scanning position can be directly obtained from the preset mapping table according to Alpha c .

[0096] In addition, although the steps of the methods in the present disclosure are described in a specific order in the drawings, this does not require or imply that these steps must be performed in that specific order, or that all the shown steps must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0097] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0098] The above are only the specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An automatic biological sample tissue morphology measurement system, characterized in that, The system includes: a controller, a tissue tray, a reflective photoelectric sensor, and an image acquisition device; the photoelectric sensor is arranged above the tissue tray; the tissue tray is a self-luminous tissue tray; both the photoelectric sensor and the image acquisition device are communicatively connected to the controller; The controller is configured to perform the following steps: According to the transparency information of the image, perform image segmentation processing on the image information of the sample to be measured collected by the image acquisition device to generate segmentation information corresponding to the image information; the segmentation information includes multiple independent image unit information; the independent image unit information includes the contour information and transparency coefficient of the independent image segmentation region; the transparency coefficient is positively correlated with transparency; If the mapped position of the current scanning position of the photoelectric sensor in the image information is located in the independent image segmentation region, then according to the transparency coefficient corresponding to the independent image segmentation region to which the mapped position belongs, obtain the amplitude threshold corresponding to the current scanning position from the preset mapping table; the transparency coefficient and the amplitude threshold in the preset mapping table are negatively correlated; Determine the signal reception time of the current scanning position as the reception time of the signal amplitude of the first signal greater than or equal to the amplitude threshold in the reflected signal waveform collected by the photoelectric sensor at the current scanning position.

2. The automatic biological sample tissue morphology measurement system according to claim 1, characterized in that, The controller is further configured to perform the following steps: Generate the thickness information d corresponding to the current scanning position of the sample to be measured according to the signal emission time t1, signal reception time t2 corresponding to the current scanning position, and the distance L between the photoelectric sensor and the tissue tray; d satisfies the following steps: ； where C is the speed of light.

3. The automatic biological sample tissue morphology measurement system according to claim 1, characterized in that, The controller is further configured to perform the following steps: Generate a three-dimensional contour model of the sample to be measured according to the thickness information corresponding to all scanning positions in the sample to be measured.

4. The automatic biological sample tissue morphology measurement system according to claim 1, characterized in that, The segmentation information includes multiple boundary image unit information; the boundary image unit information includes the contour information, first transparency coefficient, and second transparency coefficient of the boundary image segmentation region; the boundary image segmentation region includes an inward contraction region that contracts inward along the original segmentation boundary of the independent image segmentation region and an outward expansion region that expands outward along the original segmentation boundary of the independent image segmentation region; the first transparency coefficient is the transparency coefficient corresponding to the independent image segmentation region to which the inward contraction region belongs; the second transparency coefficient is the transparency coefficient corresponding to the independent image segmentation region to which the outward expansion region belongs; The controller is further configured to perform the following steps: If the mapped position of the current scanning position of the photoelectric sensor in the image information is located in the boundary image segmentation region, then according to the image information of the independent image segmentation region to which the adduction region belongs, a first predicted transparency coefficient Alpha corresponding to the current scanning position is generated 1 ; Generate a second predicted transparency coefficient Alpha corresponding to the current scanning position based on the image information of the independent image segmentation region to which the externally expanded region belongs 2 ; According to Alpha 1 and Alpha 2 , generate the reference transparency coefficient Alpha corresponding to the current scanning position c ; Alpha c satisfies the following conditions: Alpha c = (Q2 × Alpha 1 + Q1 × Alpha 2 ) / h; where h is the width of the boundary image segmentation region, Q1 is the distance from the mapped position of the current scanning position in the image information to the boundary of the inward contraction region, and Q2 is the distance from the mapped position of the current scanning position in the image information to the boundary of the outward expansion region; Take the transparency coefficient among the first transparency coefficient and the second transparency coefficient that is closest to Alpha c as the transparency coefficient corresponding to the current scanning position.

5. The automatic biological sample tissue morphology measurement system according to claim 1, characterized in that, After taking the transparency coefficient closest to Alpha among the first transparency coefficient and the second transparency coefficient as the transparency coefficient corresponding to the current scanning position, the controller is further configured to perform the following steps: c ​ According to the transparency coefficient corresponding to the current scanning position, obtain the amplitude threshold corresponding to the current scanning position from the preset mapping table; Determine the signal reception time of the current scanning position as the reception time of the signal amplitude of the first signal greater than or equal to the amplitude threshold in the reflected signal waveform collected by the photoelectric sensor at the current scanning position.

6. The automatic biological sample tissue morphology measurement system according to claim 4, characterized in that, Generate a first predicted transparency coefficient Alpha corresponding to the current scanning position based on the image information of the independent image segmentation region to which the adduction region belongs 1 , including: Use the inpaint function to generate the first predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation region to which the adduction region belongs 1 .

7. The automatic biological sample tissue morphology measurement system according to claim 4, characterized in that, Generating a first predicted transparency coefficient Alpha corresponding to the current scanning position according to the image information of the independent image segmentation region to which the adduction region belongs 1 , including: Using the interpolation method, based on the image information of the independent image segmentation region to which the adduction region belongs, generate the first predicted transparency coefficient Alpha corresponding to the current scanning position 1 .

8. The automatic biological sample tissue morphology measurement system according to claim 1, characterized in that, The image acquisition device includes: an RGB camera; the image information includes an RGB image; Performing image segmentation processing on the image information of the sample to be measured collected by the image acquisition device according to the transparency information of the image, and generating segmentation information corresponding to the image information, including: Inputting the RGB image of the sample to be measured obtained by the RGB camera into the target U-Net model to generate segmentation information corresponding to the image information.

9. An automatic biological sample tissue topography measurement system according to claim 1, characterized in that, It further includes: a lateral movement component and a longitudinal movement component; The longitudinal movement component is arranged above the lateral movement component, and the movement directions of the longitudinal movement component and the lateral movement component are perpendicular to each other; The tissue tray is arranged on the lateral movement component, and the photoelectric sensor is arranged on the longitudinal movement component; The lateral movement component includes: a first slider group, a second slider group, a lateral lead screw, a lead screw motor and a tissue tray base; The tissue tray base is arranged on the sliding parts of the first slider group and the second slider group; the driving part of the lateral lead screw is fixedly connected to the tissue tray base; the lead screw motor is fixedly connected to the lateral lead screw; the tissue tray base is used for placing the tissue tray.

10. An automatic biological sample tissue topography measurement system according to claim 9, characterized in that, The longitudinal movement component includes: a longitudinal lead screw, a lead screw motor and an optical scanning measurement mechanism bracket; The lead screw motor is connected to the longitudinal lead screw, and the optical scanning measurement mechanism bracket is connected to the sliding part of the lead screw; the photoelectric sensor is arranged on the optical scanning measurement mechanism bracket.

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