An evolutionary analysis method based on active tissue region data array index

Abstract

This invention discloses an evolutionary analysis method based on an array of indicators for active tissue regions, relating to the field of biomedical optical imaging technology. The method includes the following steps: Based on a D-FFOCT imaging system, an APMD-FFOCT imaging system is obtained by actively phase-modulating interfering signals. The APMD-FFOCT imaging system is used to acquire data from the active tissue regions of the tumor. The pure dynamic intensity of metabolic activity is extracted by decoupling the dynamic spectrum integral value after excluding zero-frequency components, and the decoupled metabolic dynamic intensity DMDI is calculated. The ratio of the decoupled metabolic dynamic intensity DMDI between the cell nucleus and cytoplasm, N / C-DMDI, is used to perform evolutionary analysis of the tumor. This invention decouples the metabolic dynamics of a scatterer from its reflectivity and other uncontrollable imaging factors, thereby enabling the analysis of its metabolic dynamics and reflectivity characteristics, providing a new technical approach for biomedical optical imaging technology.

Description

Technical Field

[0001] This invention relates to the field of biomedical optical imaging technology, and more specifically, to an evolutionary analysis method based on an array of indicators of active tissue regions. Background Technology

[0002] In the biomedical engineering industry, tumor diagnosis and grading rely on histopathological examination, and abnormal cellular metabolism has long been considered a major characteristic of cancer. Therefore, histological imaging techniques that provide rapid, high-quality imaging with metabolic contrast will be of great application value. Interferometry provides a highly sensitive mechanism for capturing the metabolic dynamics of subcellular scatterers.

[0003] Dynamic full-field optical coherence tomography (D-FFOCT) imaging is a technique used in the biomedical engineering industry. It utilizes a Linnik interferometer to measure the temporal fluctuations of backscattered light interference signals, enabling highly sensitive detection of nanoscale dynamic processes in subcellular scatterers. This technique achieves label-free and high-contrast imaging by extracting statistical features of the interference signal. However, the physical mechanism underlying the contrast in D-FFOCT imaging remains ambiguous, and the instability of the interference signal hinders the extraction of crucial information such as scatterer reflectivity and metabolic dynamics from D-FFOCT images, thus limiting the further application of D-FFOCT technology. Summary of the Invention

[0004] To address the aforementioned issues, the present invention aims to provide an evolutionary analysis method based on an array of indicators of active tissue regions. This method, through improvements to D-FFOCT technology, decouples the metabolic dynamics of the scatterer from its reflectivity and other uncontrollable imaging factors, thereby enabling the analysis of its metabolic dynamics and reflectivity characteristics and providing new technical insights for biomedical optical imaging technology.

[0005] To achieve the above technical objectives, this application provides an evolutionary analysis method based on an array of indicators of active tissue regions, comprising the following steps: The APMD-FFOCT imaging system was used to acquire data on the active tissue region of the tumor. The APMD-FFOCT imaging system is based on the D-FFOCT imaging system and is obtained by active phase modulation of the interference signal. The pure dynamic intensity of metabolic activity is extracted by decoupling the dynamic spectrum integral value after excluding the zero frequency component, and the decoupled metabolic dynamic intensity DMDI is calculated. The tumor was analyzed for evolution using the ratio of the decoupled metabolic dynamic intensity DMDI between the cell nucleus and cytoplasm, N / C-DMDI.

[0006] Optionally, the active phase modulation process of the interference signal includes: By adding a sinusoidal signal to the reference arm PZT of the D-FFOCT imaging system, active phase modulation of the interference signal is achieved.

[0007] Optionally, the active phase modulation process of the interference signal includes: Active phase modulation with a frequency of 25Hz and an amplitude of 15-30nm is used to avoid interference with metabolic kinetic signals distributed in the low-frequency region.

[0008] Optionally, the process of obtaining the decoupled metabolic dynamics intensity (DMDI) includes: The decoupled metabolic dynamic intensity (DMDI) is obtained based on the spectral components of metabolic motion and the spectral components of active phase modulation.

[0009] Optionally, the evolutionary analysis process may include: N / C-DMDI was used as a biomarker for tumor malignancy.

[0010] Optionally, the evolutionary analysis process may include: The evolutionary analysis was performed based on the positive correlation between N / C-DMDI and tumor grade.

[0011] Optionally, the process of obtaining N / C-DMDI includes: APMD-FFOCT images were acquired using the APMD-FFOCT imaging system. Convert the APMD-FFOCT image into a grayscale image; After binarizing the grayscale image, the bwareaopen function is used for image filtering to obtain the initial kernel binary image; Based on the initial nuclear binary image, the nuclear region and the cytoplasmic region are labeled respectively, and the average DMDI of the labeled region is used as the corresponding nuclear DMDI and cytoplasmic DMDI to obtain N / C-DMDI.

[0012] Optionally, the process of obtaining DMDI from cell nuclei includes: The imerode function is applied to the initial nuclear binary image to reduce the nuclear region, resulting in a nuclear binary image. The nuclear DMDI is then obtained based on the average DMDI of the nuclear binary image.

[0013] Optionally, the process of obtaining DMDI in the cytoplasm includes: Based on the initial binary nuclear image, a slight dilation process is performed to simultaneously cover the nuclear membrane and the nuclear region; A second dilation process is performed, and an intersection operation is performed with the original image to obtain a binary image of the labeled cytoplasmic region. The cytoplasmic DMDI is obtained based on the average DMDI of the labeled region.

[0014] This application also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the evolutionary analysis method based on the active tissue region data array index as described above.

[0015] The present invention discloses the following technical effects: This invention effectively decouples metabolic dynamics from reflectivity and other interfering factors in interferometry by introducing active phase modulation as a reference standard, thereby obtaining independent and biologically meaningful contrast. This enables the biomedical engineering industry to achieve label-free, high-resolution three-dimensional imaging and quantitative analysis of subcellular metabolic dynamics in freshly resected tumor tissue without additional processing. It not only presents subcellular structures comparable to H&E staining images but also provides metabolic dynamics and reflectivity information to aid in pathological interpretation. Furthermore, it helps the biomedical engineering industry identify potential biomarkers, such as nucleolar size and activity indicating tumor proliferation, and deep blue structures indicating apoptosis-like processes. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the APMD-FFOCT system and imaging principle described in this invention.

[0018] Figure 2 This is a verification of the APMD-FFOCT described in this invention in metabolic kinetic decoupling.

[0019] Figure 3 This is a schematic diagram of DMDI and reflectance analysis for analyzing IDC tumor subcellular structures as described in this invention.

[0020] Figure 4 This is a schematic diagram illustrating the time-dependent changes in DMDI and reflectivity of IDC cells as described in this invention.

[0021] Figure 5 These are APMD-FFOCT images that use different IDC grades and N / C-DMDI ratios as biomarkers of tumor malignancy, as described in this invention.

[0022] Figure 6 This is an automatic labeling map of the cell nucleus and cytoplasm regions as described in this invention.

[0023] Figure 7 This is a schematic diagram of the method described in this invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected 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.

[0025] like Figures 1-7 As shown, this invention provides an evolutionary analysis method based on an array of indicators of active tissue regions. The ratio of the decoupled metabolic dynamic intensity (DMDI) between the cell nucleus and cytoplasm (N / C-DMDI ratio) is used as an index of active tissue region data of tumors to reflect tumor characteristics in the biomedical engineering industry.

[0026] Among them, the active tissue region data array index is obtained by decoupled metabolic dynamic intensity DMDI calculation through improved dynamic full-field optical coherence tomography (D-FFOCT) imaging technology. The improved dynamic full-field optical coherence tomography (D-FFOCT) imaging technology is named APMD-FFOCT imaging technology. APMD-FFOCT imaging technology is based on traditional D-FFOCT imaging technology and achieves it by active phase modulation of interference signals. It adopts an active phase modulation scheme with a frequency of 25Hz and an amplitude of 15-30nm, which can effectively avoid interference with metabolic dynamic signals mainly distributed in the low-frequency region.

[0027] In one embodiment, the present invention discloses the APMD-FFOCT system and its imaging principle, such as... Figure 1As shown, A is a schematic diagram of APMD-FFOCT: BS beam splitter; OB water immersion objective; PZT piezoelectric level shifter; YAG yttrium aluminum garnet crystal. The signal generator provides an active phase modulation signal to drive the PZT in the reference arm. B is a schematic diagram of intracellular scatterer dynamics: the black box corresponds to the detection range of a single pixel of the camera. Intracellular scatterers have different reflectivities due to differences in size and composition. C is a schematic diagram showing the relationship between z-axis displacement, scatterer reflectivity, and interference light intensity fluctuations on the camera, where reflectivity determines the amplitude of light intensity fluctuations. D is a temporal intensity trajectory captured by the camera showing the interference signals generated by two scatterers with the same motion but different reflectivities: the black smooth trajectory represents the metabolic motion signal of the scatterer, and the colored trajectory shows the metabolic motion signal combined with active phase modulation. E is a schematic diagram of the spectrum obtained by Fourier transform of the intensity trajectory. The brightness of each pixel corresponds to the integral value of the non-zero frequency component in its spectrum, and its color reflects the position of the spectral centroid. The intensity peaks in the high-frequency region originate from the active phase modulation of the PZT.

[0028] For example, this invention achieves active phase modulation of the interference signal by adding a sinusoidal signal to the PZT of the reference arm. Using an active phase modulation scheme with a frequency of 25Hz and an amplitude of 15-30nm, interference with metabolic kinetic signals mainly distributed in the low-frequency region can be effectively avoided. The interference signal fluctuations detected by the camera simultaneously include signals generated by sample metabolic motion and the active phase modulation signal, such as... Figure 1 As shown in Figure D, the modulation signal generated by the reference mirror, in interferometry, if the reference mirror is considered stationary, this active phase modulation actually represents the collective oscillation phenomenon generated by all interferometric scatterers during their individual metabolic movements. After performing a Fourier transform on the intensity trajectory, this active phase modulation will form sharp intensity peaks in the spectrum (such as...). Figure 1(As shown in E in the figure). Since active phase modulation at 25 Hz exhibits consistent characteristics across all scatterers and significantly exceeds the metabolic kinetics at this frequency, the height of the active phase modulation peak in the spectrum primarily reflects factors such as scatterer reflectivity and imaging conditions—factors that also affect the frequency components of scatterer metabolic kinetics. To eliminate these interfering factors, the spectrum can be normalized by dividing it by the height of the active phase modulation peak. From a physical perspective, this step aims to compare metabolic motion with active modulation motion, the latter serving as a reference standard. Therefore, this comparison method eliminates the influence of all factors except inherent motion characteristics. The spectrum obtained after division reflects only inherent motion characteristics and is called the dynamic spectrum. Any motion can be considered as a superposition of simple harmonic motions at different frequencies, and the height of each point in the spectrum represents the amplitude of the harmonic motion at the corresponding frequency. The integral value of the dynamic spectrum reflects the cumulative contribution of different frequency components to the overall motion. Thus, the pure dynamic intensity of metabolic activity can be decoupled and extracted by excluding the zero-frequency component from the integral value of the dynamic spectrum.

[0029] Extracted by active phase modulation To decouple metabolic dynamics intensity (DMDI). and These represent the spectral components of metabolic activity and the spectral components of active phase modulation, respectively. Representing the frequency derivative, it can be seen that DMDI provides a new kind of imaging contrast, enabling the present invention to understand biological tissues from the perspective of metabolic movement characteristics.

[0030] For example, the APMD-FFOCT configuration employs a Linnik interferometer equipped with a pair of 20x water immersion objectives and a low-coherence light source. Light emitted from the LED light source (model M565L3, Thorlabs, center wavelength 565 nm, full width at half maximum 104 nm) is distributed to the sample arm and reference arm via a 50 / 50 unpolarized beam splitter (BS013, Thorlabs), and then focused onto the back focal plane of the objectives for Kohler illumination. To obtain an objective numerical aperture of approximately 0.7 μm², this invention uses a pair of identical water immersion objectives (Nikon NIRAPO 20×0.5NA). The lateral resolution is approximately 500×350 µm². The light power illuminating the sample is 1.2 mW.

[0031] Exemplarily, this invention uses water as a medium to achieve refractive index matching, thereby reducing reflections from the objective lens and coverslip surfaces. Simultaneously, this design minimizes the distance between the focal plane and the coherence plane when adjusting imaging depth. The axial resolution is approximately 1 μm, determined by the coherence length of the light source. In the sample arm, freshly excised tissue samples are secured to a custom-designed sample holder. The tissue is kept moist with saline solution and gently pressed with a coverslip to create a flatter imaging surface. The coverslip height is adjustable via a rotating thread and locked in place by a snap-fit ​​mechanism. The sample holder is then mounted on a five-dimensional control platform with three-dimensional motorized translation and two-dimensional angle adjustment capabilities, ensuring the coverslip remains parallel to the focal plane and maintaining consistent imaging depth during lateral movement of the translation stage. The reference mirror (YAG, yttrium aluminum garnet) in the reference arm can be modulated via a PZT (TA0505D024W, Thorlabs) below to generate phase modulation during imaging. The entire reference arm is mounted on a high-precision motorized translation platform (M-VP-25XL, Newport) to adjust the optical path difference between the two arms. The objective lens focal plane must coincide with the coherence plane to achieve optimal imaging performance. Backscattered light from the subcellular scatterer and the reference mirror is re-converged by a beam splitter and then focused onto the camera (MV4-D1600-S01-GT, Photon Focus) by a tube lens. The entire system is mounted on an active vibration isolation platform (VCM-S400) to reduce the impact of environmental vibrations.

[0032] For example, such as Figure 2 As shown, A represents the D-FFOCT image of agarose gel containing titanium dioxide particles (200nm-10μm) under 10Hz harmonic motion. B represents the motion spectrum extracted from eight randomly selected scatterers, showing the peak values ​​at 10Hz (sample motion) and 25Hz (active phase modulation). C and D represent the comparison of the sample motion peak height and the ratio of decoupling peak values ​​with changes in scatterer reflectivity, light intensity, and modulation amplitude, respectively. F represents the images of the glycolysis inhibition effect at 0 minutes and 130 minutes after treatment with 5mM 2-deoxy-D-glucose (2-DG) on freshly excised tumor tissue. G represents the curves of the average DMDI and reflectivity extracted from the cell region in Figure F as a function of time. H represents the images of the control group tumor tissue at 0 minutes and 130 minutes after treatment with physiological saline. I represents the curves of the average DMDI and reflectivity of the control group in Figure H as a function of time.

[0033] In one embodiment, such as Figure 2As shown, to verify the decoupling capability of APMD-FFOCT, this invention constructs an agarose gel model embedded with titanium dioxide (TiO2) particles of different diameters (200 nm to 10 μm) to simulate intracellular scatterers of different sizes. Given the uncontrollable and difficult-to-quantify nature of intracellular metabolic motion, this phantom model enables precise experimental control. PZT is used to uniformly drive the motion of all embedded scatterers. Since any complex metabolic motion can be decomposed into a sequence of harmonic components of different frequencies, verifying the decoupling effect of single-frequency harmonic motion proves the effectiveness of this method. Therefore, this invention applies a 10 Hz resonant oscillation signal to the phantom to synchronize the motion of all scatterers. Figure 2 As shown in A, traditional D-FFOCT images exhibit significant brightness variations due to differences in the reflectivity of the scatterers. Eight scatterers of different sizes were randomly selected, and their interference signal spectra were extracted. Figure 2 (B in the text). The 10Hz peak corresponds to the applied sample motion, while the 25Hz peak reflects the active phase modulation generated by the reference arm. Figure 2 The C-value in the figure compares the sample motion peak height (representing luminance in conventional D-FFOCT) with the decoupled peak ratio (defined as the ratio of the 10Hz to the 25Hz peak, representing DMDI) of eight scatterers. Although all scatterers have the same motion characteristics, their original peak heights differ significantly (257–756) due to differences in reflectivity. Conversely, the decoupled peak ratio remains stable (0.29–0.31). Figure 2 As shown in D, the peak height increases linearly with increasing illumination intensity, while the peak ratio remains almost constant. This indicates that the decoupling index is independent of reflectivity and illumination power. In contrast, Figure 2 The peak height and peak ratio of the E-display increase proportionally with the sample modulation amplitude. These results confirm that the DMDI obtained by APMD-FFOCT can effectively escape the influence of reflectivity and illumination conditions, and mainly depends on the amplitude of the scatterer motion.

[0034] In one embodiment, such as Figure 2 As shown, to verify that DMDI can effectively reflect the level of metabolic activity in biological tissues, this invention performed time-lapse imaging analysis on two groups of freshly resected tumor samples. One group of samples was treated with 5 mM glycolysis inhibitor 2-deoxy-D-glucose (2-DG) to inhibit metabolic activity (…). Figure 2 The F group, and the control group, used physiological saline. Figure 2 (H in the original text). For both groups of samples, the average DMDI value and reflectance of the cellular region over time were extracted. (e.g., H). Figure 2 G and Figure 2As shown in Figure I, the 2-DG treatment group exhibited a significant decreasing trend in DMDI values, indicating reduced metabolic activity; while the control group showed no significant change in DMDI values ​​during the same time period. In summary, these results confirm that the DMDI provided by APMD-FFOCT can serve as a reliable indicator of tissue metabolic activity, unaffected by interfering factors such as diffuse reflectance and light intensity.

[0035] For example, such as Figure 3 As shown, A is a reflectance comparison image generated by the active phase modulation portion of the spectrum (blue area in Figure K). B is a reflectance-DMDI coupled comparison image generated by the spectrum of the green area in Figure K. C is a DMDI comparison image. D is a three-dimensional image of the tumor cell nucleus in the tissue. E is a composite image of A and B. FI is a magnified view of the area within the red box in AD, highlighting specific intracellular features. Blue arrows point to the dark blue area within the cell nucleus, red arrows point to the nuclear membrane, black arrows point to the black circular area, green arrows point to the remaining part of the cell nucleus, and purple dashed boxes indicate the cytoplasm. J is the H&E histological image of the same sample. K is the spectral analysis of collagen fibers, cell nuclei, and cytoplasm in AE. The intensity noise caused by environmental vibration is mainly distributed in the 40Hz to 50Hz frequency band, which shows a regular distribution at each location. L shows the spectrum corresponding to the structures indicated by the blue, black, and green arrows in the figure, and the cytoplasm within the purple dashed box in FI. M represents the statistical analysis of the brightness (reflectance × DMDI), reflectance, and DMDI of structures in the FI plot (sample size N=36). The bar chart corresponds to structures marked with the same color in the FI plot. The error bars represent ±1 standard deviation (SD). N is a schematic diagram showing the distribution of reflectance and DMDI in tumor cells.

[0036] In one embodiment, such as Figure 3 As shown, the distribution and morphology of the extracellular matrix and tumor cells play a crucial role in tumor diagnosis and grading. This invention uses APMD-FFOCT technology to image freshly resected invasive ductal carcinoma (IDC, grade III) samples and reveals the cellular and stroma structures from a metabolic kinetic perspective. Figure 3 A and B in the text correspond to respectively Figure 3 The integral values ​​of the blue and green regions in the K-spectrum can approximate the reflectivity contrast and reflectivity-dynamic coupling contrast, the latter corresponding to the brightness characteristics of a traditional D-FFOCT image. Figure 3 C in the figure shows a DMDI contrast image obtained based on active phase modulation decoupling technology. Figure 3 A distinct dark area is visible in the central region of A, and its distribution is similar to... Figure 3The consistent position of the nuclei observed in B indicates that these nuclei have a lower reflectivity than the surrounding cytoplasm. Notably, despite the lower reflectivity, these nuclei... Figure 3 The strongest signal intensity was still observed in C, indicating extremely high metabolic activity. Furthermore, collagen fibers are mainly distributed in... Figure 3 The edge region of A exhibits the highest reflectivity. However, Figure 3 The region corresponding to C in the spectrum exhibits the lowest signal intensity, indicating that although tumor-associated collagen fibers are ubiquitous as high backscattering structures, their metabolic dynamics are significantly reduced compared to tumor cells. Considering that well-aligned collagen fibers are a biomarker for survival in human breast cancer, but are often difficult to observe in traditional two-photon FFOCT (D-FFOCT), this study integrates... Figure 3 A and B can simultaneously reveal the distribution morphology of collagen fibers and tumor cells, thereby obtaining, for example Figure 3 The image shown in Figure E is a more detailed and comprehensive diagnostic image. This demonstrates that active phase modulation technology can induce motion signals in stationary tissues, making them visible in D-FFOCT imaging.

[0037] Exemplary, the present invention can obtain three-dimensional APMD FFOCT images of fresh ex vivo tissue containing cell nuclear components by acquiring images layer by layer along the z-axis and filtering out high-spectrum (collagen fibers) and low-signal regions (cytoplasm). Figure 3 (As shown in D in the diagram). Therefore, the internal structure of the cell nucleus is presented more clearly.

[0038] For example, by utilizing the reflectivity and dynamic information provided by active phase modulation, the present invention can understand and infer these structural features from multiple dimensions. Figure 3 As shown in M ​​and N, the brightness, reflectance, and DMDI values ​​of the 36 tumor cells in the sample can be mainly divided into four types ( Figure 3 In the FI section, black, blue, and green arrows and purple dashed boxes are used to indicate the structures, with the green arrows and purple dashed boxes representing the cell nucleus and cytoplasm, respectively. Figure 3 The black arrows in the middle G indicate the circular and black areas, and their three-dimensional structure is as follows: Figure 3 The image in Figure I shows spherical cavities. These structures are highly similar to the spherical nucleoli observed in H&E-stained images of the same sample. Figure 3 (J in the text). The nucleolus has a relatively dense solid structure within the cell nucleus, exhibiting higher reflectivity and lower dynamic characteristics compared to the surrounding nucleoplasm. These characteristics are highly consistent with the reflectivity and dynamic features obtained by APMD-FFOCT technology (e.g., Figure 3As shown in MN), this further confirms that the black circular region corresponds to the nucleolar structure. It is well known that the size and activity of the nucleolar are significantly correlated with the degree of tumor proliferation. Therefore, this new technology enables rapid, label-free detection of proliferation-related biomarkers. Figure 3 In the diagram, the red arrow in the letter G points to the black line inside the tumor cell nucleus, while... Figure 3 The three-dimensional image in section I shows that these structures completely separate the cell nucleus. Red arrows mark the overlapping nuclear membrane structures of adjacent nuclei in multinucleated cells, indicating the high pleomorphism of the nucleus. Furthermore, as shown by the blue arrows, several irregular dark blue structures are distributed within the cell nucleus. Figure 3 The MN data show that these structures exhibit the lowest reflectivity and the highest dynamic characteristics, suggesting that their structures are relatively loose, concentrated, or diluted.

[0039] For example, such as Figure 4 As shown, AC are APM FDFC images of IDC tumors at 0, 30, and 70 minutes, with the inset showing a magnified view of the selected cells within the box. D represents the ratio of the dark blue area to the total area of ​​the cell nucleus over time, corresponding to the selected cells in image AC. EG are APM FDFC images of breast tumors at 0, 3, and 12 hours, with the inset showing a magnified view of the highlighted cells within the box. HI shows the temporal trend of DMDI and reflectance of the cells shown in the inset of image EG.

[0040] In one embodiment, such as Figure 4 As shown, APMD-FFOCT, a label-free imaging technique, utilizes a low-power illumination system, thus enabling long-term observation of tumor cells. This technique is particularly suitable for studying dynamic processes in freshly resected tissue. These dynamic processes provide additional information for identifying deep blue structures. One-hour observation showed that deep blue structures initially appeared in the center of the cell nucleus and gradually expanded outwards (e.g., ...). Figure 4 As shown in AC in the diagram). During this period, the proportion of the dark blue region to the entire core area continued to increase (as shown in AC in the diagram). Figure 4 (D in the middle). Figure 4A 12-hour observation of EG cells revealed the differentiated fate of these tumor cells: Cell 4 (marked with a red box) maintained structural integrity throughout the observation, with its brightness gradually decreasing, indicating reduced metabolic activity (possibly due to gradual energy depletion); while Cell 5 (marked with an orange box) exhibited a dark blue structure in the center of the nucleus, which gradually expanded outwards. Ultimately, this cell entered a disordered state with decreasing brightness, indicating cell death. Cell 6 (marked with a blue box) initially showed a uniform nuclear structure, but after 3 hours, a dark blue structure appeared in the center of the nucleus. Finally, this tumor cell exhibited a dim, blurred structure, also indicating cell death. In fact, these processes closely resemble apoptosis—chromatin condenses at the periphery of the nucleus, followed by nucleus division into multiple membrane-bound vesicles. Therefore, the dark blue structure represents diluted nucleoplasm remaining after peripheral chromatin condensation, consistent with its low reflectivity and high dynamic characteristics. Figure 4 The quantitative analysis of H and I showed the trends of mean DMDI and reflectance of cells 4-6 over time. Figure 4 As shown in H, the DMDI of cell 4 initially exhibited a plateau phase lasting approximately 4 hours, followed by a gradual and sustained decline. Meanwhile, Figure 4 In the study, the reflectance of cell 4 in group I showed no significant trend before 10 hours, then slightly increased, indicating that its structure had not changed significantly. Conversely, cells 5 and 6 showed a rapid decrease in DMDI in the first 6 hours, followed by a stable low-intensity plateau. As the DMDI of cells 5 and 6 decreased, their reflectance showed an increasing trend. Specifically, the reflectance of cell 5 increased continuously for about 10 hours before entering a plateau phase, while the reflectance of cell 6 remained stable for the first 3 hours, then rapidly increased with the appearance of the deep blue structure, entering a gradual growth phase. This invention suggests that the rapid decrease in DMDI corresponds to a reduction in metabolic activity during apoptosis, while the increase in reflectance is related to the condensation of perinuclear chromatin and subsequent nuclear fragmentation to form membrane-bound vesicles. The transition from a loose, homogeneous state to a dense, discontinuous state increases the refractive index difference, leading to increased reflectance. Therefore, the appearance of the deep blue structure can serve as a biomarker for apoptosis. This method enables continuous monitoring of tumor cell apoptosis in tissues without the need for labeling and avoids the risk of phototoxicity.

[0041] For example, such as Figure 5As shown, A and C are APMD-FFOCT images of IDC samples from levels III, II, and I, respectively. DF is a magnified view of the area within the red dashed box in A. GI is the H&E staining image of the corresponding A sample. J is the DMDI statistical result of the nucleus and cytoplasm of the corresponding A sample; each data point represents the mean DMDI of the nucleus or cytoplasm in the sub-image of the stitched image. Error bar: ±1 standard deviation. K is the N / C-DMDI ratio corresponding to the J plot. L is the N / C-DMDI ratio as the Nottingham histology score increases; each data point represents the mean of an independent sample.

[0042] In one embodiment, such as Figure 5 As shown in AC, this invention obtained APMD-FFOCT images of grade III, II, and I IDC samples. The histological tumor grading of the samples was determined based on the Nottingham Histological Score derived from their H&E staining images, which is considered the gold standard. The analysis of this invention begins with the morphological structure of tumor cells. Figure 5 As shown in Figure D, APMD-FFOCT images of grade III IDCs exhibit larger nuclear areas and more pronounced nuclear pleomorphism. Furthermore, the tumor cells show more and larger nucleoli, indicating a lower degree of differentiation and stronger malignant behavior in the samples. Figure 5 The H&E staining images corresponding to G in the data confirm the observations of this invention. (Level II IDC) Figure 5 The middle E-cell image shows a medium-sized nucleus with few and small nucleoli, suggesting a low degree of malignancy. Figure 5 The H&E staining images corresponding to H in the middle H show similar structural features. Level I IDC Figure 5 The middle F-type nucleus shows the smallest nuclear size, and the nucleolus is almost invisible, which is consistent with... Figure 5 The histological images of the intermediate I are consistent. Therefore, despite the different contrast, APMD-FFOCT images still provide structural detail consistent with H&E stained images.

[0043] As previously mentioned, APMD-FFOCT can provide biomarkers of biological processes from a metabolic perspective, which prompted this invention to explore metabolic kinetic biomarkers for predicting clinical tumor grading. This invention extracted DMDI from the nuclei and cytoplasm of cells from these three samples. Figure 5 As shown in Figure J, the nuclear DMDI of grade III samples was higher than that of other grades, while the cytoplasmic DMDI was highest in grade I samples. However, no clear trend was observed among different tumor grades. This is because basal metabolic rate (which affects the metabolic activity of the nucleus and cytoplasm) varies due to individual patient factors (including genetic makeup, age, sex, hormone levels, and lifestyle). Therefore, absolute DMDI cannot directly reflect tumor characteristics.

[0044] For example, to further reduce the impact of individual differences in basal metabolic rate, this invention extracts the DMDI ratio between the cell nucleus and cytoplasm (N / C - DMDI ratio), such as... Figure 5 As shown in Table K. The results showed that the N / C-DMDI ratio was positively correlated with tumor grade. Subsequently, this invention conducted supplementary experiments in 21 independent patient-derived invasive ductal carcinoma (IDC) samples, as shown in Table 1.

[0045] Table 1

[0046] exist Figure 5 In the L1 dataset, each data point represents the mean N / C-DMDI ratio of a single sample. Statistical analysis showed that this ratio was positively correlated with the Nottingham histology score of the samples (r=0.74, P=1.1e). -4 The N / C-DMDI ratio is denoted as r, where r represents the Pearson correlation coefficient and P represents the significance level of the correlation. Scatter plot analysis showed that as the Nottingham histological score increased, the nuclear DMDI ratio increased (r=0.31, P=0.18), while the cytoplasmic DMDI ratio decreased (r=-0.53, P=0.013). These results confirm that the N / C-DMDI ratio is more significantly correlated with tumor grade, indicating that this indicator can serve as an effective biomarker for predicting tumor malignancy. Furthermore, this invention also explored the relationship between the N / C-DMDI ratio and nuclear area, as nuclear area is also a parameter for tumor grading. The results showed a significant correlation between the two (r=0.81, P=7e). -6 This is because both the N / C-DMDI ratio and nuclear area are derived from the same APMD-FFOCT image, thus avoiding correlation bias caused by tumor heterogeneity or sampling misalignment between H&E-stained images and APMD-FFOCT images. Notably, this correlation exhibits a non-linear trend. Specifically, nuclear DMDI values ​​increase with increasing nuclear area (r=0.61, P=0.003), while cytoplasmic DMDI values ​​show the opposite trend (r=-0.24, P=0.29). Therefore, the dependence of the N / C-DMDI ratio on nuclear area demonstrates a significantly stronger correlation and lower variance, which is more pronounced than cases where dependence is solely on nuclear or cytoplasmic DMDI. This result further confirms that the N / C-DMDI ratio can effectively reduce the influence of basal metabolic differences.

[0047] For example, the present invention has developed an automated program based on MATLAB for nuclear and cytoplasmic labeling and DMDI calculation, including the following steps: Step 10: Kernel Labeling Process. First, the APMD-FFOCT image is converted to a grayscale image and binarized to complete the initial segmentation, thereby separating the kernel component. Then, the "bwareaopen" function is used to filter out excessively small white areas in the binary image; the resulting image is the initial kernel binary image containing the pixels occupied by the labeled kernel. Next, the "imerode" function is executed on the kernel binary image to reduce the kernel region size, avoiding the influence of the nuclear membrane structure. The final kernel binary image is the kernel binary image of the central part of the labeled kernel region.

[0048] Step 20: Cytoplasmic region labeling method. First, a slight dilation process is performed on the initial binary nuclear image to simultaneously cover both the nuclear membrane and the nuclear region (avoiding nuclear membrane interference). Then, a second dilation process is performed, and an intersection operation is performed with the original image to finally obtain a binary image of the labeled cytoplasmic region.

[0049] Step 30: N / C-DMDI calculation, the final labeling result is as follows Figure 6 As shown. The nuclear and cytoplasmic DMDI refers to the average DMDI of the labeled regions in the nuclear binary image and the cytoplasmic binary image. Finally, the N / C-DMDI ratio can be obtained by dividing the nuclear DMDI by the cytoplasmic DMDI.

[0050] For example, such as Figure 6 As shown, A is a white area marking cell nuclei, where the nucleoli within the nuclei have been filtered out. Interconnected or overlapping nuclei are separated. B is a white area marking cytoplasm surrounding the nuclei. The automatic labeling operation is repeated in each sub-image of the stitched APMD FFOCT image.

[0051] In summary, this invention, through improvements to D-FFOCT technology, decouples the metabolic dynamics of the scatterer from its reflectivity and other uncontrollable imaging factors, thereby enabling the analysis of its metabolic dynamics and reflectivity characteristics, and providing a new technical approach for biomedical optical imaging technology.

[0052] In one embodiment, this application also provides a storage medium storing a computer program that, when executed by a processor, implements the steps of the evolutionary analysis method based on active tissue region data array indicators as described above.

[0053] This application provides an evolutionary analysis method and storage medium based on active tissue region data array indicators. Through a complete workflow of DC broadband optical spectrophotometry, sample scanning, time-series acquisition, and time decorrelation function analysis, it achieves non-invasive, label-free, highly stable, high-resolution, and rapid real-time determination of the state of the sample under test in the biomedical engineering industry. The core effects are significant: it can accurately capture the dynamic movement patterns of cells and subcellular structures (such as metabolic activity, movement speed and amplitude), clearly distinguish tumor cells from different types of structures such as collagen fibers and calcified tissue, and effectively improve the signal-to-noise ratio of collagen fiber regions (from 10 to 18), solving the problems of abrupt changes in tone and brightness and inconsistent stitching in traditional imaging; it can also monitor the dynamic changes of the sample in real time through time-series imaging (such as the state evolution of IDC tumor cells within 70 minutes), and combine dynamic feature quantification to accurately determine the state of cell viability, tumor benignity / malignancy, and grade, with short imaging time (1-5 seconds for a single frame acquisition, 1 second for three-dimensional virtual staining images). (Frame-by-frame imaging) and no tissue damage, eliminating the need for pathologists to manually interpret slides in the biomedical engineering industry, providing efficient and reliable technical support for intraoperative rapid diagnosis, cell activity monitoring, drug screening, and other scenarios, especially suitable for precise intraoperative assessment of diseases such as breast tumors and central nervous system tumors.

[0054] In the embodiments of the smart terminal and storage medium provided in this application, all the technical features of any of the above-described embodiments of the evolutionary analysis method based on active tissue region data array indicators may be included. The extended and explanatory content of the specification is basically the same as the various embodiments of the above method, and will not be repeated here.

[0055] This application also provides a computer program product, which includes computer program code. When the computer program code is run on a computer, it causes the computer to perform the methods described in the various possible implementations above.

[0056] This application also provides a chip, including a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that a device with the chip installed performs the methods described in the various possible implementations above.

[0057] It is understood that the above scenarios are merely examples and do not constitute a limitation on the application scenarios of the technical solutions provided in the embodiments of this application. The technical solutions of this application can also be applied to other scenarios. For example, as those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0058] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0059] The steps in the method of this application embodiment can be adjusted, combined, or deleted according to actual needs.

[0060] The units in the device of this application embodiment can be merged, divided, and deleted according to actual needs.

[0061] In this application, the same or similar terms, concepts, technical solutions and / or application scenario descriptions are generally described in detail only when they appear for the first time. When they appear again, they are generally not repeated for the sake of brevity. When understanding the technical solutions and other contents of this application, the same or similar terms, concepts, technical solutions and / or application scenario descriptions that are not described in detail later can be referred to their previous relevant detailed descriptions.

[0062] In this application, the descriptions of the various embodiments have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0063] The technical features of the present application can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present application.

[0064] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, controlled terminal, or network device, etc.) to execute the methods of each embodiment of this application.

[0065] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a storage medium or transmitted from one storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, storage disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

Claims

1. An evolutionary analysis method based on an array of indicators of active tissue regions, characterized in that, Includes the following steps: The APMD-FFOCT imaging system was used to acquire data on the active tissue region of the tumor. The APMD-FFOCT imaging system is based on the D-FFOCT imaging system and obtains the data on the active tissue region by actively modulating the interference signal. The pure dynamic intensity of metabolic activity is extracted by decoupling the dynamic spectrum integral value after excluding the zero frequency component, and the decoupled metabolic dynamic intensity DMDI is calculated. The tumor was analyzed for evolution using the ratio of the decoupled metabolic dynamic intensity DMDI between the cell nucleus and cytoplasm, N / C-DMDI.

2. The evolutionary analysis method based on active tissue region data array indicators according to claim 1, characterized in that, The active phase modulation process for interference signals includes: By adding a sinusoidal signal to the reference arm PZT of the D-FFOCT imaging system, active phase modulation of the interference signal is achieved.

3. The evolutionary analysis method based on active tissue region data array indicators according to claim 1, characterized in that, The active phase modulation process for interference signals also includes: Active phase modulation with a frequency of 25Hz and an amplitude of 15-30nm is used to avoid interference with metabolic kinetic signals distributed in the low-frequency region.

4. The evolutionary analysis method based on active tissue region data array indicators according to claim 1, characterized in that, The process of obtaining the decoupled metabolic dynamic intensity (DMDI) includes: The decoupled metabolic dynamic intensity (DMDI) is obtained based on the spectral components of metabolic motion and the spectral components of active phase modulation.

5. The evolutionary analysis method based on active tissue region data array indicators according to claim 1, characterized in that, The process of evolutionary analysis includes: N / C-DMDI was used as a biomarker for tumor malignancy.

6. The evolutionary analysis method based on active tissue region data array indicators according to claim 1, characterized in that, The evolutionary analysis process also includes: The evolutionary analysis was performed based on the positive correlation between N / C-DMDI and tumor grade.

7. The evolutionary analysis method based on active tissue region data array indicators according to any one of claims 1-6, characterized in that, The process of obtaining N / C-DMDI includes: APMD-FFOCT images were acquired using the APMD-FFOCT imaging system. Convert the APMD-FFOCT image into a grayscale image; After binarizing the grayscale image, the bwareaopen function is used for image filtering to obtain the initial kernel binary image; Based on the initial nuclear binary image, the nuclear region and the cytoplasmic region are labeled respectively, and the average DMDI of the labeled region is used as the corresponding nuclear DMDI and cytoplasmic DMDI to obtain N / C-DMDI.

8. The evolutionary analysis method based on active tissue region data array indicators according to claim 7, characterized in that, The process of obtaining DMDI from cell nuclei includes: The imerode function is applied to the initial nuclear binary image to reduce the nuclear region, resulting in a nuclear binary image. The nuclear DMDI is then obtained based on the average DMDI of the nuclear binary image.

9. The evolutionary analysis method based on active tissue region data array indicators according to claim 7, characterized in that, The process of obtaining DMDI from cytoplasm also includes: Based on the initial binary nuclear image, a slight dilation process is performed to simultaneously cover the nuclear membrane and the nuclear region; A second dilation process is performed, and an intersection operation is performed with the original image to obtain a binary image of the labeled cytoplasmic region. The cytoplasmic DMDI is obtained based on the average DMDI of the labeled region.

10. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the evolutionary analysis method based on active tissue region data array indicators as described in any one of claims 1-9.

Images