A method and apparatus for identifying the extent of a lesion in the urinary system using interferometric optics

Abstract

The application provides a method and device for identifying a lesion range of a urinary system by using interference optics, and the method comprises the following steps: obtaining a polarized image corresponding to a multi-layer optical rotation tomogram by filtering through a polaroid, and generating an original polarization image containing interference fringes and tissue texture; performing line extraction, intersection removal, Gaussian mask and smoothing reconstruction processing on the original polarization image to generate an interference bright line image; according to the light and dark distribution of the interference bright line image, optical rotation calculation and Mueller matrix correction are sequentially performed to generate tissue optical rotation distribution data; and the lesion range is identified by matching the tissue optical rotation distribution data with a preset calibration sample. Through multi-wavelength orthogonal polarization interference and optical rotation accurate detection, the ability to distinguish flat lesions and inflammatory mucosa is improved from the root, and the lesion boundary identification accuracy and identification efficiency are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of optical recognition technology, and specifically to a method and apparatus for identifying the extent of lesions in the urinary system using interferometric optics. Background Technology

[0002] In transurethral resection (TURBT / TURP) or flexible ureteroscopic surgery for urinary tract tumors (such as bladder cancer and renal pelvis cancer), accurate determination of lesion boundaries directly affects tumor residual rate and patient prognosis. In current clinical practice, the visual contrast between early-stage flat lesions (CIS) and inflammatory mucosa is often less than 5%, and even experienced urologists have an accuracy rate of less than 65% under white light endoscopy. This visual indistinguishability leads to two clinical dilemmas: first, insufficient resection results in tumor residuality; second, excessive resection of normal muscle layer in the pursuit of radical cure leads to ureteral stricture, bladder perforation, or postoperative functional impairment. While traditional intraoperative frozen section biopsy is the gold standard, it suffers from sampling errors, tissue damage, and a 15-20 minute time delay, failing to meet the real-time navigation requirements of endoscopic surgery. Summary of the Invention

[0003] In view of the aforementioned problems, this application is made to provide a method and apparatus for identifying the extent of urinary system lesions using interferometric optics to overcome or at least partially solve the aforementioned problems, comprising: A method for identifying the extent of lesions in the urinary system using interferometric optics, wherein the method uses at least four sets of orthogonally polarized interference beams and multi-wavelength lasers to irradiate the surgical area tissue to construct a multi-layer optical rotation tomography map, comprising the following steps: Obtain the polarization image corresponding to the multi-layer optical rotation tomography generated by polarizer filtering, and generate the original polarization image containing interference fringes and tissue texture from the polarization image; The original polarization image is processed by line extraction, intersection removal, Gaussian masking and smoothing reconstruction to generate an interference bright line image; Based on the brightness distribution of the interference bright line image, optical rotation calculation and Mueller matrix correction are performed sequentially to generate tissue optical rotation distribution data; The extent of lesions is identified by matching the tissue optical rotation distribution data with preset calibration samples.

[0004] Furthermore, it also includes: An orthogonal polarization interference layout is formed with at least four beams to obtain an interference light field that meets the orthogonal angle requirement; wherein, of the four beams, two beams are orthogonal to each other, and the other two beams are at a 45° angle to the orthogonal beams and are orthogonal to each other; Injecting multi-wavelength lasers into the interference light field yields orthogonally polarized interference illumination light with multi-wavelength characteristics; The surgical area tissue is irradiated with the multi-wavelength orthogonally polarized interference illumination light to generate interference fringe signals carrying tissue optical rotation information; The interference fringe signal is subjected to orthogonal phase modulation to convert the optical rotation difference into a phase shift, thereby generating a multi-layer optical rotation tomography.

[0005] Further, the step of generating the polarization image corresponding to the multi-layer optical rotation tomography spectrum by filtering with a polarizer to obtain the original polarization image containing interference fringes and tissue texture includes: The polarizer grating direction is adjusted to be parallel to any one of the four sets of interference lines to generate a filter condition with a fixed polarization detection angle. The interference light reflected from the surgical area tissue is subjected to polarization filtering using the aforementioned filtering conditions to generate a polarization image that retains only the target polarization component. Interference fringes and continuous texture of human tissue are extracted from the polarized image to generate an original polarized image containing complete texture features.

[0006] Further, the step of performing line extraction, intersection removal, Gaussian masking and smoothing reconstruction on the original polarization image to obtain the interference bright line image includes: The original polarization image is processed to extract continuous bright lines and texture to obtain a first-level line image; The primary line image is binarized, and the intersection points of the interference fringes are located using a Hough accumulator. The intersection interference of the primary line image is removed to generate the secondary line image. By using a Gaussian mask to mask the intersection regions of the secondary line image, a tertiary line image without intersection interference is generated. Global smoothing and coherence reconstruction are performed on the three-level line images to generate complete and continuous interference bright line images.

[0007] Further, the step of calculating optical rotation and correcting the Mueller matrix sequentially based on the brightness distribution of the interference bright line image to obtain tissue optical rotation distribution data includes: Preliminary optical rotation angle data are generated by analyzing the brightness distribution of the interference bright line image; The preliminary optical rotation angle data is corrected for dichroism and polarization absorption errors using the Mueller matrix to generate a high-precision single-point optical rotation value. Extreme value removal and weighted averaging were performed on multiple sets of test results to obtain stable tissue optical rotation distribution data across the entire domain.

[0008] Further, the step of identifying the lesion extent by matching the tissue optical rotation distribution data with a preset calibration sample includes: The optical rotation distribution data of the tissue is differentiated to extract the optical rotation variation characteristics and generate a derivative contour plot; The derivative contour map is matched with the preset lesion sample data to identify suspected lesion areas; Based on the matching results, the suspected lesion areas are marked to obtain the extent of lesions in the urinary system tissues.

[0009] Furthermore, it also includes: The identified lesion extent is overlaid with the real-time surgical field of view to form a visual navigation image of the lesion boundary.

[0010] A device for identifying the extent of urinary system lesions using interferometric optics, wherein the device uses at least four sets of orthogonally polarized interferometric light beams and multi-wavelength lasers to irradiate the surgical area tissue, constructing a multi-layer optical rotation tomographic map; the device implements the steps of the method for identifying the extent of urinary system lesions using interferometric optics described above: include: The original polarization image module is used to generate the polarization image corresponding to the multi-layer optical rotation tomography spectrum through polarizer filtering, and to generate the original polarization image containing interference fringes and tissue texture. The interference bright line image module is used to perform line extraction, intersection point removal, Gaussian mask and smoothing reconstruction processing on the original polarization image to generate an interference bright line image; The tissue optical rotation distribution data module is used to calculate the optical rotation and correct the Mueller matrix sequentially based on the brightness distribution of the interference bright line image to generate tissue optical rotation distribution data. The identification module is used to identify the lesion range by matching the tissue optical rotation distribution data with a preset calibration sample.

[0011] A computer electronic device includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method described in any of the preceding descriptions.

[0012] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the preceding claims.

[0013] This application has the following advantages: In the embodiments of this application, compared with the problems of existing technologies that rely on white light endoscopy for urinary system lesion identification, have low contrast, insufficient identification accuracy, sampling errors in frozen biopsy, tissue damage, and long time consumption, and cannot achieve real-time, non-contact, and unmarked lesion determination during surgery, this application provides a method for identifying the range of urinary system lesions using interferometric optics. The method uses at least four sets of orthogonally polarized interference light and multi-wavelength lasers to irradiate the surgical area tissue to construct a multi-layer optical rotation tomography map, including the following steps: acquiring a polarization image corresponding to the multi-layer optical rotation tomography map generated by filtering with a polarizer, and generating an original polarization image containing interference fringes and tissue texture from the polarization image; performing line extraction, intersection point removal, Gaussian masking, and smoothing reconstruction on the original polarization image to generate an interference bright line image; performing optical rotation calculation and Mueller matrix correction sequentially based on the brightness distribution of the interference bright line image to generate tissue optical rotation distribution data; and identifying the lesion range by matching the tissue optical rotation distribution data with a preset calibration sample. By using multi-wavelength orthogonal polarization interferometry and precise optical rotation detection, the ability to distinguish between flat lesions and inflammatory mucosa is fundamentally improved, significantly increasing the accuracy of lesion boundary identification. This enables non-contact, label-free, non-invasive, and real-time optical biopsy during surgery, eliminating the need to wait for frozen section pathology results and preventing damage to normal tissue. It avoids tumor residue and excessive resection, while also being compatible with urological endoscopic surgery scenarios. It can be directly integrated with existing endoscopic systems, with low modification difficulty and fast response speed, balancing high accuracy in lesion identification, real-time operation during surgery, clinical safety, and wide applicability. Attached Figure Description

[0014] To more clearly illustrate the technical solution of this application, the drawings used in the description of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating the steps of a method for identifying the extent of urinary system lesions using interferometric optics, as provided in an embodiment of this application. Figure 2 This is a structural block diagram of a device for identifying the extent of urinary system lesions using interferometric optics, provided in one embodiment of this application. Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention; 1. Computer equipment; 2. External devices; 3. Processing unit; 4. Bus; 5. Network adapter; 6. I / O interface; 7. Display; 8. Memory; 9. Random access memory; 10. Cache memory; 11. Storage system; 12. Program / utility; 13. Program module. Detailed Implementation

[0016] To make the objectives, features, and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0017] The inventors, through analysis of existing technologies, discovered that the current visual indistinguishability leads to two clinical dilemmas: first, insufficient resection results in tumor residue; second, excessive resection of normal muscle layer in the pursuit of radical cure can cause ureteral stricture, bladder perforation, or postoperative functional impairment. Although traditional intraoperative frozen section biopsy is the gold standard, it suffers from sampling errors, tissue damage, and a 15-20 minute time delay, failing to meet the real-time navigation requirements of endoscopic surgery.

[0018] In any embodiment of this application, Reference Figure 1 This application illustrates a method for identifying the extent of urinary system lesions using interferometric optics, characterized in that the method uses at least four sets of orthogonally polarized interference beams and multi-wavelength lasers to irradiate the surgical area tissue, constructing a multi-layer optical rotation tomography map, including the following steps: S110. Obtain the polarization image corresponding to the multi-layer optical rotation tomography generated by the polarizer filtering, and generate the original polarization image containing interference fringes and tissue texture. S120. Perform line extraction, intersection removal, Gaussian mask and smoothing reconstruction on the original polarization image to generate an interference bright line image; S130. Based on the brightness distribution of the interference bright line image, the optical rotation is calculated and the Mueller matrix is ​​corrected in sequence to generate tissue optical rotation distribution data. S140. By matching the tissue optical rotation distribution data with a preset calibration sample, the lesion range is identified.

[0019] In the embodiments of this application, compared with the problems of existing technologies that rely on white light endoscopy for urinary system lesion identification, have low contrast, insufficient identification accuracy, sampling errors in frozen biopsy, tissue damage, and long time consumption, and cannot achieve real-time, non-contact, and unmarked lesion determination during surgery, this application provides a method for identifying the range of urinary system lesions using interferometric optics. The method uses at least four sets of orthogonally polarized interference light and multi-wavelength lasers to irradiate the surgical area tissue to construct a multi-layer optical rotation tomography map, including the following steps: acquiring a polarization image corresponding to the multi-layer optical rotation tomography map generated by filtering with a polarizer, and generating an original polarization image containing interference fringes and tissue texture from the polarization image; performing line extraction, intersection point removal, Gaussian masking, and smoothing reconstruction on the original polarization image to generate an interference bright line image; performing optical rotation calculation and Mueller matrix correction sequentially based on the brightness distribution of the interference bright line image to generate tissue optical rotation distribution data; and identifying the lesion range by matching the tissue optical rotation distribution data with a preset calibration sample. By using multi-wavelength orthogonal polarization interferometry and precise optical rotation detection, the ability to distinguish between flat lesions and inflammatory mucosa is fundamentally improved, significantly increasing the accuracy of lesion boundary identification. This enables non-contact, label-free, non-invasive, and real-time optical biopsy during surgery, eliminating the need to wait for frozen section pathology results and preventing damage to normal tissue. It avoids tumor residue and excessive resection, while also being compatible with urological endoscopic surgery scenarios. It can be directly integrated with existing endoscopic systems, with low modification difficulty and fast response speed, balancing high accuracy in lesion identification, real-time operation during surgery, clinical safety, and wide applicability.

[0020] It should be noted that after the surgical area tissue is irradiated with multi-wavelength orthogonal polarization interference illumination, a polarization-initiated image corresponding to the multi-layer optical rotation tomography spectrum is obtained by filtering with a polarizer, and an original polarization image containing interference fringes and tissue texture is generated. Specifically, the polarizer grating direction is adjusted to be parallel to any one of the four sets of interference lines to generate a filter condition with a fixed polarization detection angle; the interference light reflected from the surgical area tissue is polarized and filtered using this filter condition to generate a polarization-initiated image that retains only the target polarization component; then, interference fringes and continuous texture of human tissue are extracted from the polarization-initiated image, and finally an original polarization image containing complete texture features is generated, providing basic data with rich optical information for subsequent processing; The original polarization image is processed by line extraction, intersection point removal, Gaussian masking and smoothing reconstruction to generate an interference bright line image: First, continuous bright lines and tissue texture are extracted from the original image to obtain a first-level line image. After binarization and Hough accumulator to locate the intersection points, interference is removed to generate a second-level line image. Then, the intersection point area is shielded by Gaussian mask to obtain a third-level line image. Finally, after global smoothing and coherence reconstruction, an interference-free, continuous and complete interference bright line image is obtained. Based on the brightness distribution of the interference bright line image, the optical rotation is calculated and the Mueller matrix is ​​corrected in sequence to generate tissue optical rotation distribution data: First, preliminary optical rotation angle data is generated based on the brightness distribution, and then the dichroism and polarization absorption errors are corrected by the Mueller matrix to obtain high-precision single-point optical rotation values. After extreme value elimination and weighted averaging, stable tissue optical rotation distribution data is formed. The acquired tissue optical rotation distribution data is matched with preset calibration samples. Specifically, the tissue optical rotation distribution data is first differentiated to extract optical rotation change features and generate a derivative contour map. Then, the contour map is matched with the preset lesion sample data to determine the suspected lesion area. Finally, the suspected lesion area is marked according to the matching results, so as to intuitively and accurately obtain the lesion range of the urinary system tissue and complete the entire non-contact, non-marking lesion identification process.

[0021] As described in step S110, a polarization image corresponding to the multi-layer optical rotation tomography generated by polarizer filtering is obtained, and an original polarization image containing interference fringes and tissue texture is generated from the polarization image.

[0022] It should be noted that the surgical area tissue of the urinary system is irradiated with orthogonally polarized interference light generated by multi-wavelength lasers, producing an optical signal carrying the tissue's optical rotation information. This signal is then filtered by a polarizer to obtain a polarization image corresponding to a multi-layer optical rotation tomography map. This polarization image has undergone polarization component screening, eliminating stray polarized light interference, and can accurately reflect the differences in optical rotation and the distribution of interference light on the tissue surface.

[0023] Based on the acquired polarization image, the system further extracts interference fringe features and continuous texture information of the human tissue itself, fusing the two types of features to generate the original polarization image. This original image simultaneously preserves the regular interference bright line structure and the irregular physiological texture of the tissue, providing complete and reliable raw data for subsequent line extraction and optical rotation calculation.

[0024] By using polarizer directional filtering and texture feature preservation, this step effectively enhances the visualization of tissue optical rotation information, transforming subtle differences in optical rotation into calculable and analyzable changes in image brightness and stripes. This solves the problem of extremely low contrast between lesions and normal tissues under white light endoscopy, making them difficult to distinguish, and lays the image foundation for achieving non-contact, label-free, and real-time lesion recognition.

[0025] In one embodiment of the present invention, the specific process of step S110, "obtaining the polarization image corresponding to the multi-layer optical rotation tomography generated by polarizer filtering, and generating the original polarization image containing interference fringes and tissue texture," can be further explained in conjunction with the following description.

[0026] As described in the following steps, the polarizer grating direction is adjusted to be parallel to any one of the four sets of interference lines to generate a filter condition with a fixed polarization detection angle; As described in the following steps, the interference light reflected from the surgical area tissue is subjected to polarization filtering using the filtering conditions to generate a polarization image that retains only the target polarization component; As described in the following steps, interference fringes and continuous texture of human tissue are extracted from the polarized image to generate an original polarized image containing complete texture features.

[0027] It should be noted that adjusting the polarizer grating to be parallel to any one of the four sets of interference lines generates a stable and uniform fixed polarization angle filtering condition, ensuring consistent polarization signal angles and comparability of subsequent acquisitions. Using this filtering condition to perform polarization filtering on the interference light reflected from the surgical area tissue effectively removes stray light and non-target polarization components, retaining only the effective polarization information matching the interference light field, thus generating a polarization image with high signal-to-noise ratio and clear features. Further extraction of interference fringes and continuous texture of human tissue from the polarization image allows for the simultaneous preservation of regular interference signals and irregular tissue physiological characteristics, generating an original polarization image containing complete texture features. This provides a realistic and complete image foundation for subsequent line extraction, optical rotation calculation, and lesion identification.

[0028] In one embodiment of the present invention, it further includes: An orthogonal polarization interference layout is formed with at least four beams to obtain an interference light field that meets the orthogonal angle requirement; wherein, of the four beams, two beams are orthogonal to each other, and the other two beams are at a 45° angle to the orthogonal beams and are orthogonal to each other; Injecting multi-wavelength lasers into the interference light field yields orthogonally polarized interference illumination light with multi-wavelength characteristics; The surgical area tissue is irradiated with the multi-wavelength orthogonally polarized interference illumination light to generate interference fringe signals carrying tissue optical rotation information; The interference fringe signal is subjected to orthogonal phase modulation to convert the optical rotation difference into a phase shift, thereby generating a multi-layer optical rotation tomography.

[0029] It should be noted that, firstly, an orthogonal polarization interference layout is constructed with at least four beams to form an interference light field that meets the orthogonal angle requirements; among the four beams, two beams are orthogonal to each other to form an orthogonal group, and the remaining two beams are at a 45° angle to the orthogonal group and are orthogonal to each other, thus establishing the basis of an interference light field with uniform angle distribution and comprehensive polarization coverage, providing a stable and reliable optical environment for subsequent optical rotation detection.

[0030] By injecting selected multi-wavelength lasers into the aforementioned interference light field, an interference illumination light with both multi-wavelength and orthogonal polarization characteristics is obtained. This illumination light can utilize the differences in penetration depth of different wavelengths into urinary system tissues to achieve layered optical detection from the surface to the deep layer, providing multi-dimensional and multi-depth optical information for constructing multi-layer optical rotation tomography.

[0031] By illuminating the surgical area with multi-wavelength orthogonal polarization interference light, interference fringe signals carrying tissue optical rotation information are generated. Then, the interference fringe signals are orthogonally phase modulated to convert the weak differences in optical rotation between tissues into observable and calculable interference fringe phase shifts. Finally, a multi-layer optical rotation tomography map that can reflect the optical rotation characteristics of tissues at different depths is generated, providing core data support for accurate lesion identification.

[0032] As described in step S120, the original polarization image is processed by line extraction, intersection removal, Gaussian masking and smoothing reconstruction to generate an interference bright line image.

[0033] It should be noted that the original polarization image is first processed by line extraction. The Steger algorithm is used to accurately extract continuous interference bright lines and human tissue texture from the image, obtaining clear and continuous first-level line features. This transforms the messy polarization signal into an analyzable line structure, laying the foundation for subsequent processing.

[0034] Next, the extracted line image is processed by intersection point removal and Gaussian masking. First, the intersection points of the interference fringe grid are located and the interference is removed by binarization and Hough accumulator. Then, the noise and abnormal bright spots in the intersection area are shielded by Gaussian masking to obtain a secondary line image with no intersection interference and a higher signal-to-noise ratio, ensuring that the bright line features are pure and reliable.

[0035] Finally, global smoothing and coherence reconstruction are performed on the processed line image. The continuity and integrity of the bright lines are restored by the smoothing formula, and regular and coherent interference grid lines are reconstructed. In the end, a continuous, smooth and interference-free bright line image is generated, which provides a high-quality image basis for subsequent optical rotation calculation.

[0036] In one embodiment of the present invention, the specific process of "extracting lines, removing intersections, performing Gaussian masking and smoothing reconstruction on the original polarization image to generate an interference bright line image" in step S120 can be further described in conjunction with the following description.

[0037] As described in the following steps, continuous bright lines and textures are extracted from the original polarization image to obtain a first-level line image; It should be noted that this step uses the Steger algorithm to achieve sub-pixel level line extraction, which can accurately identify the continuous contours of interference fringes and tissue textures, preserve the complete line shape and position information, and avoid texture breakage or loss of details.

[0038] As described in the following steps, the primary line image is binarized and the intersection points of the interference fringes are located using a Hough accumulator. The intersection interference of the primary line image is removed to generate the secondary line image. It should be noted that the Hough accumulator can efficiently locate strong interference points such as grid intersections. By eliminating intersections, distortion and noise at line intersections can be eliminated, improving the accuracy of subsequent optical rotation calculations.

[0039] As described in the following steps, the intersection regions of the secondary line image are masked using a Gaussian mask to generate a tertiary line image without intersection interference; It should be noted that the Gaussian mask uses a weighted smoothing method to shield the intersection area, which can remove interference while preserving the transition characteristics of the surrounding bright lines, avoiding abrupt line interruptions caused by hard shielding.

[0040] As described in the following steps, global smoothing and coherence reconstruction are performed on the third-level line image to generate a complete and continuous interference bright line image.

[0041] It should be noted that global smoothing and coherence reconstruction can repair local defects, keeping the interference bright lines uniform, continuous and regular, providing a stable and reliable image basis for subsequent optical rotation resolution.

[0042] As described in step S130, based on the brightness distribution of the interference bright line image, optical rotation calculation and Mueller matrix correction are performed sequentially to generate tissue optical rotation distribution data.

[0043] It should be noted that the brightness distribution of the bright lines in the interference image directly reflects the difference in optical rotation intensity of the tissue. By calculating the brightness values ​​of the bright lines, the optical rotation angle data can be obtained initially. Then, by using the Mueller matrix to strictly correct for systematic errors such as dichroism and polarization absorption, as well as tissue interference, the accuracy of optical rotation detection can be significantly improved. Finally, stable, reliable, and true-to-life tissue optical rotation distribution data that reflects the physiological state of the tissue are obtained, providing core quantitative basis for lesion identification.

[0044] In one embodiment of the present invention, the specific process of step S130, "based on the brightness distribution of the interference bright line image, sequentially performing optical rotation calculation and Mueller matrix correction to generate tissue optical rotation distribution data," can be further explained in conjunction with the following description.

[0045] As described in the following steps, preliminary optical rotation angle data are generated from the brightness distribution of the interference bright line image; It should be noted that the changes in brightness of the bright lines are directly determined by the differences in optical rotation of the tissue. By combining Malus's law and the derivation of Stokes' parameters, the light intensity distribution can be converted into the optical rotation angle, thus obtaining basic data reflecting the preliminary optical rotation characteristics of the tissue.

[0046] As described in the following steps, the preliminary optical rotation angle data is corrected for dichroism and polarization absorption errors using the Mueller matrix to generate a high-precision single-point optical rotation value. It should be noted that the Mueller matrix can eliminate measurement biases caused by tissue dichroism, differential absorption, and optical path system, restore the apparent polarization angle to the true optical rotation, and greatly improve the accuracy of single-point measurement.

[0047] As described in the following steps, extreme value removal and weighted averaging are performed on multiple sets of detection results to obtain stable tissue optical rotation distribution data across the entire domain.

[0048] It should be noted that removing outliers and averaging by weight can reduce the impact of noise and random errors, making the optical rotation data across the entire domain smooth, consistent, stable and reliable, providing high-precision quantitative support for lesion range identification.

[0049] As described in step S140, the lesion range is identified by matching the tissue optical rotation distribution data with a preset calibration sample.

[0050] It should be noted that the preset calibration samples are standard image data in which the lesion range has been accurately marked by pathology experts, which can provide a reliable reference for matching optical rotation features. By comparing and matching the tissue optical rotation distribution data with the calibration samples, the lesion area corresponding to the optical rotation abnormality can be accurately identified, thereby realizing the automatic determination and marking of the lesion range.

[0051] In one embodiment of the present invention, the specific process of "identifying the lesion range by matching the tissue optical rotation distribution data with a preset calibration sample" in step S140 can be further explained in conjunction with the following description.

[0052] As described in the following steps, the tissue optical rotation distribution data is differentiated to extract optical rotation variation features and generate derivative contour plots; It should be noted that abrupt changes and steep changes in optical rotation correspond to the boundaries of tissue lesions. By taking the derivative, minute differences in optical rotation can be amplified to form clear contour lines, accurately distinguishing normal tissue from lesion areas.

[0053] As described in the following steps, the derivative contour map is matched with the preset lesion sample data to determine the suspected lesion area; It should be noted that the preset samples are calibrated by pathology experts. During the matching process, the optical rotation change characteristics that closely match the lesions can be automatically identified, thereby improving the accuracy of identification and reducing misjudgment and missed judgment.

[0054] As described in the following steps, the suspected lesion areas are marked according to the matching results to obtain the lesion range of the urinary system tissue.

[0055] It should be noted that the annotation results are intuitive, quantitative, and traceable, providing doctors with clear references for lesion boundaries and enabling non-contact, non-invasive intraoperative optical biopsy.

[0056] In one embodiment of the present invention, the method further includes overlaying the identified lesion range with the real-time surgical field of view to form a visual navigation image of the lesion boundary.

[0057] It should be noted that overlay navigation can guide surgical procedures in real time, helping doctors to precisely control the extent of resection, avoiding tumor residue and preventing excessive removal of normal tissue, thereby improving surgical safety and prognosis.

[0058] As an example, this solution identifies the extent of urinary system lesions through the following steps: 1. Utilize laser interferometry to generate four sets of parallel patterns to irradiate the surgical area tissue. At least four sets are required, with the first and second sets orthogonal, and the third set forming a 45° acute angle with both the first and second sets. The fourth set is orthogonal to the third set. Employing a four-beam orthogonal polarization interferometry architecture, utilize the differences in tissue penetration depth (~0.3mm, ~0.7mm, ~1.2mm, ~2.0mm inurothelium respectively) at different wavelengths (405nm blue light, 532nm green light, 650nm red light, and 940nm near-infrared light are recommended) to construct multi-layer optical rotation tomography. Through orthogonal phase modulation, the optical rotation difference can be converted into a phase shift of the interference fringes, amplifying weak optical anisotropy signals. 2. The system receives an image with a field of view of 12*12mm. The received image is a polarized image, meaning the image has already been filtered by a polarizer. 3. Ensure that the direction of the polarizer grating is parallel to any one of the four sets of interference lines; 4. Use the Steger algorithm to extract continuous lines, finding bright lines and continuous textures of human tissue; the formula for the Steger algorithm is:

[0059] This formula is an iterative estimation formula for sub-pixel edge positions based on image gradients. Wherein, The calculated sub-pixel level precise coordinates; The current pixel-level coordinates (integer coordinates) are the starting point for the calculation, which is usually the integer pixel position where the gradient maximum value is located in the image; This is the gradient vector of the image at that point; This represents the grayscale value (or intensity value) of the image at that point. Let be the square of the magnitude of the gradient vector (the square of the 2-norm of the gradient), and

[0060] (Horizontal gradient) is the first-order partial derivative of image intensity (grayscale) with respect to the x-coordinate, representing the horizontal rate of change; (Vertical gradient) is the first-order partial derivative of image intensity (gray level) with respect to the y-coordinate, and the vertical rate of change; I is the gray-level function of the image; x and y are the spatial coordinate components of the image.

[0061] 5. Binarize the image, and use a Hough accumulator to find and remove the grid intersections of the interference fringes.

[0062] First, the cumulative algorithm is used to find the bright lines that are distorted due to the unevenness of the organ tissue cross-section, and then compared with the lines obtained in method 4. The confidence weights are 30% for method 4 and 70% for method 5. in, For accumulators, the voting count in the parameter space; This is a distance parameter, representing the perpendicular distance from the line to the origin. The angle parameter is the angle between the straight line normal and the x-axis. To obtain the summation sign, sum all edge points; i is the index, the sequence number of the edge point; N is the total number of edge points; for Function, voting indicator function; Let x be the x-coordinate, and let x be the x-coordinate of the i-th edge point. Let be the y-coordinate, and let be the ordinate of the i-th edge point; Let be the cosine, and be the x-component of the normal. For sine, the component of the normal in the y-direction; 6. Use the matrix method to find the coordinates of the intersection point;

[0063]

[0064] Cramer's rule is used to find the coordinates of the intersection point of two lines. The mathematical expression for, where, The x-coordinate of the intersection point of the lines is . The ordinate of the intersection point of the two lines; the subscript variables (distinguishing N lines): subscripts 1 and 2 in the formula represent two different lines respectively, and so on, subscript 1 represents the parameter of the first line L1; subscript 2 represents the parameter of the second line L2; input known quantities, which are the line parameters detected by the Hough transform: and This is the vertical distance from the line to the origin of the coordinate system (usually the top left or bottom left corner of the image). The distance from the first line to the origin. is the distance from the second line to the origin; , are the cosine value of the corresponding angle and the sine value of the corresponding angle, which form the direction of the normal vector of the line and are used to determine the orientation of the line.

[0065] The denominator determinant (common denominator) Cramer matrix Solving linear equations by determinants is the determinant part:

[0066] This is the determinant of the matrix composed of the coefficients of two linear equations. Using trigonometric identities; it reflects the included angle size between two lines. If the denominator D = 0, it means the two lines are parallel and have no intersection point (or the intersection point is at infinity), and the formula is meaningless at this time. It is necessary to judge whether the denominator is 0 before calculation; The numerator determinant:

[0067] Composition method: Replace the first column of the denominator determinant with the constant terms of the system of equations; it combines the mixed terms of distance parameters and angle parameters and is used to calculate the x-coordinate component; The numerator determinant:

[0068] Composition method: Replace the second column of the denominator determinant with the constant terms of the system of equations; it combines the mixed terms of distance parameters and angle parameters and is used to calculate the y-coordinate component; Obtain the coordinates of all intersection points of the four groups of lines.

[0069] 7. Sum up and locate and mark all the intersection points obtained in step 6: <000026!>

[0070] Among them, {} is the set construction symbol; are the specific intersection point coordinates, | means "satisfy" or "such that"; are any two lines in the line set; is the set of all lines detected by the previous step transformation; i < j is the constraint relationship of the index size. It can avoid repeated calculation. The intersection point of line Li and Lj is equivalent to the intersection point of Lj and Li. Stipulating i < j can ensure that each pair of combinations is calculated for the intersection point only once (a combination problem, not a permutation problem). At the same time, it avoids invalid calculation: preventing the situation of i = j (that is, the same line intersects itself, which is meaningless).

[0071] 8. Use Gaussian masking to exclude the interference of bright line intersection points:

[0072] Among them, It is a Gaussian weighted mask function; It is an exponential function with the natural constant as its base; It is twice the variance in the denominator of the Gaussian function.

[0073] 9. Use a smoothing formula to globally restore the continuity of bright lines and reconstruct grid integrity. Remove masks that cover intersections:

[0074] in, P is the global mask; P is a single intersection element in the set. M is the set of all intersection points; p (x, y) is a single mask generated for the i-th intersection point.

[0075] After the above processing, the distribution of the four sets of bright lines and the spatial distribution of brightness changes are finally obtained.

[0076] 10. Since the image received by the CCD receiving end is a polarized light image, the optical rotation can be calibrated based on the brightness distribution of the four sets of bright lines. After passing through the material, the light intensity becomes I. source At angle t, the polarization direction changes to α, and then the light passes through the analyzer (polarizer). According to Malus's law, when the analyzer angle is α... At that time, the transmitted light intensity I ( )for I ( ) = (I source ·t)·cos 2 ( -α) The four sets of superimposed interference parallel lines correspond to analysis angles of 0°, 45°, 90°, and 135°. Substituting these into the above formula to derive the Stokes parameters, we obtain the following system of equations: I N =K / 2(1+sin2α·cosα), (N is 0°, 45°, 90°, 135°) K=I source ·t Comparison and correction of any two sets of equations

[0077] Where 45 should be N, and N is 0°, 45°, 90°, 135°, and N1≠N2, αalpha is the optical rotation, thus we get:

[0078] S1 and S2 are coordinates. Any two sets of calculations are cross-corrected to obtain six sets of optical rotation angles. After removing extreme values, the average value is taken to obtain the optical rotation angle of each point on the bright line of the image.

[0079] 11. Using the Mueller matrix for rigorous correction, optical rotation / polarization: alpha α (to be solved); dichroism (differential absorption): the tissue has different emissivity for two orthogonal polarization directions. Let the reflectivity of the tissue along the "fast axis" (or low absorption axis) be t1, and the reflectivity along the "slow axis" (high absorption axis) be t2. The Mueller matrix M of the tissue can be expressed as the product of the optical rotation matrix and the dichroism matrix; since t1 and t2 are known, R is a constant; M = R(-α)

[0080] The reflected light emission vector is E out :

[0081] The polarization angle (azimuth angle) φ of the reflected light is no longer equal to the optical rotation, but is an "apparent angle" jointly determined by α, t1, and t2:

[0082] t1cos 2 cos 2 +t2sin 2 sin 2 +1 / 2 =0 Because the polarizer's direction is parallel to one of the four sets of lines, and it is parallel to one axis of the orthogonal coordinate system.

[0083] make The ratio of the light intensity of any two sets of lines is calculated as follows:

[0084] Taking the square root of both sides and the arctangent, we obtain the apparent polarization angle, which is the polarization direction after absorption, not the true optical rotation.

[0085] Substituting η and φ into the expression:

[0086] Any two sets of calculations are cross-corrected to obtain six sets of optical rotation angles. After removing extreme values, the average value is taken to obtain the optical rotation angle of each point on the bright line of the image.

[0087] 12. Using the derivatives of the weighted confidence curves of optical rotation from steps 10 and 11, connect the points where the derivatives are consistent to form a derivative contour plot. The weights are set to 20% for the confidence level of the result from step 10 and 80% for the confidence level of the result from step 11.

[0088] 13. Using images with lesion extents marked by pathologists, and combining them with contour images of polarized light reflection changes from step 11, train the identification of lesions. Because lesions such as urinary tract inflammation, invasive tumors, or fibrosis, which are difficult to distinguish with the naked eye, can cause inflammatory reactions in the lesion area, leading to abnormal glucose metabolism or abnormal small molecule protein synthesis, the difference in polarized light generated by reflection can effectively distinguish changes in cell fluid concentration of 8*10^-8 mol / L. This allows for accurate marking of the lesion area, guiding physicians in intraoperative, non-invasive pathological diagnosis and accurate lesion removal. This not only effectively prevents excessive damage from over-removal but also avoids the harm caused by incomplete removal of residual lesions, leading to disease recurrence or poor postoperative efficacy.

[0089] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0090] Reference Figure 2 This application illustrates an embodiment of a device for identifying the extent of urinary system lesions using interferometric optics. The device uses at least four sets of orthogonally polarized interferometric beams and multi-wavelength lasers to irradiate the surgical area tissue, constructing a multi-layer optical rotation tomography map. The device implements the steps of the method described above for identifying the extent of urinary system lesions using interferometric optics. include: The original polarization image module 210 is used to acquire the polarization image corresponding to the multi-layer optical rotation tomography generated by the polarizer filtering, and to generate an original polarization image containing interference fringes and tissue texture through the polarization image. The interference bright line image module 220 is used to perform line extraction, intersection point removal, Gaussian mask and smooth reconstruction processing on the original polarization image to generate an interference bright line image. The tissue optical rotation distribution data module 230 is used to calculate the optical rotation and correct the Mueller matrix sequentially based on the brightness distribution of the interference bright line image to generate tissue optical rotation distribution data. The identification module 240 is used to identify the lesion range by matching the tissue optical rotation distribution data with a preset calibration sample.

[0091] In one embodiment of the present invention, a preprocessing module is further included, the preprocessing module comprising: An orthogonal layout submodule is used to form an orthogonal polarization interference layout with at least four beams to obtain an interference light field that meets the orthogonal angle requirements; wherein, of the four beams, two beams are mutually orthogonal to each other to form an orthogonal group, and the other two beams are respectively at a 45° angle to the orthogonal group and are mutually orthogonal. An orthogonal polarization interference illumination photonic module is used to inject multi-wavelength lasers into the interference light field to obtain orthogonal polarization interference illumination light with multi-wavelength characteristics; The interference fringe signal submodule is used to generate an interference fringe signal carrying the optical rotation information of the tissue by irradiating the surgical area tissue with the multi-wavelength orthogonally polarized interference illumination light. The multi-layer optical rotation tomography submodule is used to perform orthogonal phase modulation on the interference fringe signal, converting the optical rotation difference into a phase shift, and generating a multi-layer optical rotation tomography.

[0092] In one embodiment of the present invention, the original polarization image module 210 includes: The filtering condition submodule is used to adjust the polarizer grating direction to be parallel to any one of the four sets of interference lines, generating filtering conditions with a fixed polarization detection angle. The polarization image submodule is used to perform polarization filtering on the interference light reflected from the surgical area tissue using the filtering conditions, and generate a polarization image that retains only the target polarization component. The original polarization image submodule is used to extract interference fringes and continuous texture of human tissue from the polarized image to generate an original polarization image containing complete texture features.

[0093] In one embodiment of the present invention, the interference bright line image module 220 includes: The primary line image submodule is used to extract continuous bright lines and texture from the original polarization image to obtain a primary line image; The secondary line image submodule is used to binarize the primary line image and use a Hough accumulator to locate the intersection points of interference fringes, and remove the intersection interference of the primary line image to generate the secondary line image. The third-level line image submodule is used to mask the intersection areas of the second-level line image using a Gaussian mask to generate a third-level line image without intersection interference. The interference bright line image submodule is used to perform global smoothing and coherence reconstruction on the third-level line image to generate a complete and continuous interference bright line image.

[0094] In one embodiment of the present invention, the tissue optical rotation distribution data module 230 includes: The preliminary optical rotation angle data submodule is used to generate preliminary optical rotation angle data based on the brightness distribution of the interference bright line image; The high-precision single-point optical rotation value submodule is used to correct the dichroism and polarization absorption errors of the preliminary optical rotation angle data using the Mueller matrix, and generate a high-precision single-point optical rotation value. The tissue optical rotation distribution data submodule is used to perform extreme value removal and weighted averaging on multiple sets of detection results to obtain globally stable tissue optical rotation distribution data.

[0095] In one embodiment of the present invention, the identification module 240 includes: The derivative contour plot submodule is used to perform derivative processing on the tissue optical rotation distribution data, extract optical rotation variation features, and generate a derivative contour plot. The suspected lesion area submodule is used to perform feature matching between the derivative contour map and the preset lesion sample data to determine the suspected lesion area; The lesion extent submodule is used to mark the suspected lesion areas based on the matching results, thereby obtaining the lesion extent of the urinary system tissues.

[0096] In one embodiment of the present invention, a navigation module is also included, which is used to overlay the identified lesion range with the real-time surgical field of view to form a visual navigation image of the lesion boundary.

[0097] Reference Figure 3 The diagram illustrates a computer device for implementing a method of identifying the extent of urinary system lesions using interferometric optics, which may specifically include the following: The aforementioned computer device 1 is in the form of a general-purpose computing device. The components of the computer device 1 may include, but are not limited to: one or more processors or processing units 3, memory 8, and a bus 4 connecting different system components (including memory 8 and processing unit 3).

[0098] Bus 4 represents one or more of several bus architectures, including memory buses or memory controllers, peripheral buses, graphics acceleration ports, processors, or local buses using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Audio / Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0099] Computer device 1 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 1, including volatile and non-volatile media, removable and non-removable media.

[0100] Memory 8 may include computer system readable media in the form of volatile memory, such as random access memory 9 and / or cache memory 10. Computer device 1 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 11 may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard disk drive"). Although Figure 3As not shown, a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 4 via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 13 configured to perform the functions of the embodiments of this application.

[0101] A program / utility 12 having a set (at least one) of program modules 13 may be stored, for example, in memory. Such program modules 13 include—but are not limited to—an operating system, one or more application programs, other program modules 13, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 13 typically perform the functions and / or methods described in the embodiments of this application.

[0102] Computer device 1 can also communicate with one or more external devices 2 (e.g., keyboard, pointing device, monitor 7, camera, etc.), and with one or more devices that enable an operator to interact with computer device 1, and / or with any device that enables computer device 1 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through I / O interface 6. Furthermore, computer device 1 can communicate with one or more networks (e.g., local area network (LAN)), wide area network (WAN), and / or public networks (e.g., the Internet) through network adapter 5, and can also exchange data via medical data network protocols such as ICD protocol, DICOM protocol, and HL7 protocol. Figure 3 As shown, network adapter 5 communicates with other modules of computer device 1 via bus 4. It should be understood that, although... Figure 3 Not shown, it can be combined with computer device 1 to use other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing unit 3, external disk drive array, RAID system, tape drive and data backup storage system 11, etc.

[0103] The processing unit 3 executes various functional applications and data processing by running programs stored in memory 8, such as implementing a method for identifying the extent of urinary system lesions using interference optics provided in the embodiments of this application.

[0104] That is, when the above-mentioned processing unit 3 executes the above-mentioned program, it achieves the following: obtaining the polarization image corresponding to the multi-layer optical rotation tomography generated by the polarizer filtering, and generating the original polarization image containing interference fringes and tissue texture; The original polarization image is processed by line extraction, intersection removal, Gaussian masking and smoothing reconstruction to generate an interference bright line image; Based on the brightness distribution of the interference bright line image, optical rotation calculation and Mueller matrix correction are performed sequentially to generate tissue optical rotation distribution data; The extent of lesions is identified by matching the tissue optical rotation distribution data with preset calibration samples.

[0105] In this application embodiment, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for identifying the extent of urinary system lesions using interferometric optics as provided in all embodiments of the present application.

[0106] That is, when the program is executed by the processor, it achieves the following: obtaining the polarization image corresponding to the multi-layer optical rotation tomography generated by the polarizer filtering, and generating the original polarization image containing interference fringes and tissue texture; The original polarization image is processed by line extraction, intersection removal, Gaussian masking and smoothing reconstruction to generate an interference bright line image; Based on the brightness distribution of the interference bright line image, optical rotation calculation and Mueller matrix correction are performed sequentially to generate tissue optical rotation distribution data; The extent of lesions is identified by matching the tissue optical rotation distribution data with preset calibration samples.

[0107] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0108] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0109] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the operator's computer, partially on the operator's computer, as a standalone software package, partially on the operator's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the operator's computer via any type of network—including a local area network (LAN) or wide area network (WAN) that is compatible with medical network standards such as HL7 for HIS, RIS, and LIS systems—or it can be connected to an external computer (e.g., via the Internet using an Internet service provider). The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably.

[0110] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0111] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0112] The above provides a detailed description of the method and apparatus for identifying the extent of urinary system lesions using interferometric optics. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for identifying the extent of lesions in the urinary system using interferometric optics, characterized in that, The method uses at least four sets of orthogonally polarized interference beams and multi-wavelength lasers to irradiate the surgical area tissue, constructing a multi-layer optical rotation tomography map, including the following steps: Obtain the polarization image corresponding to the multi-layer optical rotation tomography generated by polarizer filtering, and generate the original polarization image containing interference fringes and tissue texture from the polarization image; The original polarization image is processed by line extraction, intersection removal, Gaussian masking and smoothing reconstruction to generate an interference bright line image; Based on the brightness distribution of the interference bright line image, optical rotation calculation and Mueller matrix correction are performed sequentially to generate tissue optical rotation distribution data; The extent of lesions is identified by matching the tissue optical rotation distribution data with preset calibration samples.

2. The method according to claim 1, characterized in that, Also includes: An orthogonal polarization interference layout is formed with at least four beams to obtain an interference light field that meets the orthogonal angle requirement; wherein, of the four beams, two beams are orthogonal to each other, and the other two beams are at a 45° angle to the orthogonal beams and are orthogonal to each other; Injecting multi-wavelength lasers into the interference light field yields orthogonally polarized interference illumination light with multi-wavelength characteristics; The surgical area tissue is irradiated with the multi-wavelength orthogonally polarized interference illumination light to generate interference fringe signals carrying tissue optical rotation information; The interference fringe signal is subjected to orthogonal phase modulation to convert the optical rotation difference into a phase shift, thereby generating a multi-layer optical rotation tomography.

3. The method according to claim 1, characterized in that, The step of generating the polarization image corresponding to the multi-layer optical rotation tomography spectrum by filtering with a polarizer, and obtaining the original polarization image containing interference fringes and tissue texture, includes: The polarizer grating direction is adjusted to be parallel to any one of the four sets of interference lines to generate a filter condition with a fixed polarization detection angle. The interference light reflected from the surgical area tissue is subjected to polarization filtering using the aforementioned filtering conditions to generate a polarization image that retains only the target polarization component. Interference fringes and continuous texture of human tissue are extracted from the polarized image to generate an original polarized image containing complete texture features.

4. The method according to claim 1, characterized in that, The steps of performing line extraction, intersection removal, Gaussian masking, and smoothing reconstruction on the original polarization image to obtain an interference bright line image include: The original polarization image is processed to extract continuous bright lines and texture to obtain a first-level line image; The primary line image is binarized, and the intersection points of the interference fringes are located using a Hough accumulator. The intersection interference of the primary line image is removed to generate the secondary line image. By using a Gaussian mask to mask the intersection regions of the secondary line image, a tertiary line image without intersection interference is generated. Global smoothing and coherence reconstruction are performed on the three-level line images to generate complete and continuous interference bright line images.

5. The method according to claim 1, characterized in that, The step of calculating optical rotation and correcting the Mueller matrix sequentially based on the brightness distribution of the interference bright line image to obtain tissue optical rotation distribution data includes: Preliminary optical rotation angle data are generated by analyzing the brightness distribution of the interference bright line image; The preliminary optical rotation angle data is corrected for dichroism and polarization absorption errors using the Mueller matrix to generate a high-precision single-point optical rotation value. Extreme value removal and weighted averaging were performed on multiple sets of test results to obtain stable tissue optical rotation distribution data across the entire domain.

6. The method according to claim 1, characterized in that, The step of identifying the lesion range by matching the tissue optical rotation distribution data with a preset calibration sample includes: The optical rotation distribution data of the tissue is differentiated to extract the optical rotation variation characteristics and generate a derivative contour plot; The derivative contour map is matched with the preset lesion sample data to identify suspected lesion areas; Based on the matching results, the suspected lesion areas are marked to obtain the extent of lesions in the urinary system tissues.

7. The method according to claim 1, characterized in that, Also includes: The identified lesion extent is overlaid with the real-time surgical field of view to form a visual navigation image of the lesion boundary.

8. A device for identifying the extent of lesions in the urinary system using interferometric optics, characterized in that, The device uses at least four sets of orthogonally polarized interference beams and multi-wavelength lasers to irradiate the surgical area tissue, constructing a multi-layer optical rotation tomographic map; the device implements the steps of the method for identifying the extent of urinary system lesions using interferometric optics as described in any one of claims 1-7: include: The original polarization image module is used to generate the polarization image corresponding to the multi-layer optical rotation tomography spectrum through polarizer filtering, and to generate the original polarization image containing interference fringes and tissue texture. The interference bright line image module is used to perform line extraction, intersection point removal, Gaussian mask and smoothing reconstruction processing on the original polarization image to generate an interference bright line image; The tissue optical rotation distribution data module is used to calculate the optical rotation and correct the Mueller matrix sequentially based on the brightness distribution of the interference bright line image to generate tissue optical rotation distribution data. The identification module is used to identify the lesion range by matching the tissue optical rotation distribution data with a preset calibration sample.

9. A computer electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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