Cross-polarized Images Research Articles

Integrated deep learning approach for generating cross-polarized images and analyzing skin melanin and hemoglobin distributions.

Cross-polarized images are beneficial for skin pigment analysis due to the enhanced visualization of melanin and hemoglobin regions. However, the required imaging equipment can be bulky and optically complex. Additionally, preparing ground truths for training pigment analysis models is labor-intensive. This study aims to introduce an integrated approach for generating cross-polarized images and creating skin melanin and hemoglobin maps without the need for ground truth preparation for pigment distributions. We propose a two-component approach: a cross-polarized image generation module and a skin analysis module. Three generative adversarial networks (CycleGAN, pix2pix, and pix2pixHD) are compared for creating cross-polarized images. The regression analysis network for skin analysis is trained with theoretically reconstructed ground truths based on the optical properties of pigments. The methodology is evaluated using the VISIA VAESTRO clinical system. The cross-polarized image generation module achieved a peak signal-to-noise ratio of 35.514dB. The skin analysis module demonstrated correlation coefficients of 0.942 for hemoglobin and 0.922 for melanin. The integrated approach yielded correlation coefficients of 0.923 for hemoglobin and 0.897 for melanin, respectively. The proposed approach achieved a reasonable correlation with the professional system using actually captured images, offering a promising alternative to existing professional equipment without the need for additional optical instruments or extensive ground truth preparation.

A Novel Method for the Estimation of Sea Surface Wind Speed from SAR Imagery

Wind is one of the important environmental factors influencing marine target detection as it is the source of sea clutter and also affects target motion and drift. The accurate estimation of wind speed is crucial for developing an efficient machine learning (ML) model for target detection. For example, high wind speeds make it more likely to mistakenly detect clutter as a marine target. This paper presents a novel approach for the estimation of sea surface wind speed (SSWS) and direction utilizing satellite imagery through innovative ML algorithms. Unlike existing methods, our proposed technique does not require wind direction information and normalized radar cross-section (NRCS) values and therefore can be used for a wide range of satellite images when the initial calibrated data are not available. In the proposed method, we extract features from co-polarized (HH) and cross-polarized (HV) satellite images and then fuse advanced regression techniques with SSWS estimation. The comparison between the proposed model and three well-known C-band models (CMODs)—CMOD-IFR2, CMOD5N, and CMOD7—further indicates the superior performance of the proposed model. The proposed model achieved the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), with values of 0.97 m/s and 0.62 m/s for calibrated images, and 1.37 and 0.97 for uncalibrated images, respectively, on the RCM dataset.

Unsupervised topological analysis of polarized light microscopy: application to quantitative birefringence imaging.

The determination of birefringence (magnitude and axis orientation) of optical materials is of significant interest in various fields. In the case of composite samples, this task becomes complicated and time-consuming; therefore, a partially automated procedure for reconstructing birefringence spatial distribution becomes valuable. Herein, we propose a procedure to reconstruct the spatial distributions of the retardance and optical axis orientation in a geological thin section from sparse quantitative birefringence measurements, using automatic boundary detection on cross-polarized light microscopy images. We examine two particular areas on the selected geological thin section: one that presents a uniaxial crystal with a circular cross-section of its refractive index ellipsoid and the other with grains of varying orientations. The measurement gives the orientation of the grain's optical axis both in and out of the plane of the thin section, which explains the qualitative observations with the cross-polarized light microscope. Future work will connect the measured orientation of the rock thin section with its 3D geological orientation in the field.

Significantly Enhanced Improvement in Dryness, Roughness, Fine Lines and Radiance Following Daily Use of a Novel Multi-Weight Hyaluronic Acid Plus Antioxidant Complex-Based Lotion Compared to a Single-Weight HA Plus Ceramide-Based Lotion

Introduction: Hyaluronic acid (HA) has become a commonly used ingredient in many topical moisturizing products due to its strong humectant properties and essential role in skin hydration; however, limitations of delivery of HA to only the surface of skin has hindered leveraging the full capacity of HA biology necessary for skin rejuvenation. Here we describe the head-to-head clinical comparison of a novel multi-weight HA plus antioxidant complex-based lotion with SPF 30 and a single-weight plus ceramide-based lotion with SPF 30 for clinical efficacy on dryness, roughness, fine facial lines, and radiance following daily use. Methods: A double-blind comparative study was conducted on 70 female subjects (n=35, multi-weight HA plus antioxidant complex-based lotion with SPF 30; n=35, single-weight plus ceramide-based lotion with SPF 30) ages 25 to 65 years with mild to moderate facial dryness and visible fine lines and wrinkles, including subjects with Fitzpatrick Skin Types I-VI, with 20% having Fitzpatrick Skin Types V-VI. Clinical grading of the face including dryness, roughness, and fine lines were assessed at baseline, 30 minutes, 2 weeks, 4 weeks, and 8 weeks after once daily application in the morning. Visible, cross-polarized (X-Pol), parallel-polarized (P-Pol) and UV fluorescence clinical images were acquired for each time point. X-Pol and P-Pol images were used to quantify skin radiance. Results: Daily use of the multi-weight HA plus antioxidant complex-based lotion with SPF 30 demonstrated significant improvements in all clinical grading assessments of dryness, fine lines, and wrinkles as early as Week 2 compared to baseline. Improvements in visible dryness (Week 2), roughness (Week 2), and fine lines (Week 8) were greater for the multi-weight HA plus antioxidant complex-based lotion with SPF 30 compared to the single-weight HA plus ceramide-based lotion with SPF 30, with overall statistical significance across all three parameters assessed favoring the multi-weight HA plus antioxidant complex-based lotion with SPF 30. For radiance, daily use of the multi-weight HA plus antioxidant complex-based lotion with SPF 30 demonstrated significant increase in radiance at Week 4 compared with baseline, while the single-weight HA plus ceramide-based lotion did not show any improvement in skin radiance compared with baseline. Conclusions: The marked improvements in dryness, roughness, fine lines, and radiance following daily utilization of the multi-weight HA plus antioxidant complex-based lotion with SPF 30 may be attributed to the inherent properties of HA. These improvements may be further attributed to the ability of multi-weight HAs to moisturize the skin surface and penetrate the upper surface layers of the skin, combined with the added benefits of key antioxidants, including glycine saponin and glycyrrhetinic acid, which have been previously shown to induce endogenous HA synthesis and inhibit endogenous hyaluronidase activity in vitro, respectively.

Novel Approach to Wind Retrieval from Sentinel-1 SAR in Tropical Cyclones

The strong winds in tropical cyclones (TCs) are commonly retrieved from cross-polarized SAR images using a geophysical model function (GMF). However, the accuracy of wind retrieval in cross-polarization is significantly reduced at the edges of sub-swaths. In this study, a novel approach to TC wind retrieval from VV polarized SAR images is proposed based on using the azimuthal cutoff wavelength to represent the effect of velocity bunching. A total of 12 dual-polarized (VV and VH) Sentinel-1 (S-1) images acquired in the interferometric wide (IW) mode were used, five of which were collocated with measurements taken by the Stepped-Frequency Microwave Radiometer (SFMR) on board an NOAA aircraft. The SAR-based azimuthal cutoff wavelengths were found to be linearly related to the SFMR wind speeds. Based on this finding, an empirical GMF for TC wind speed retrieval from VV S-1 images was constructed. The inversion results from seven images using this approach were validated against the wind products from the Advanced Scatterometer and the European Center for Medium-Range Weather Forecasts. The RMSE of the wind speed was 2.15 m s−1 and the correlation coefficient (COR) was 0.83 at wind speeds of less than 25 m s−1, while the RMSE was 2.66 m s−1 and the COR was 0.97 when compared with wind retrieval using the VH-polarized GMF S1IW.NR at wind speeds greater than 25 m s−1. The proposed algorithm performs well and has two advantages: (1) it is not subject to the saturation problem of the VV backscattering signal and (2) the discontinuity of the retrieval results obtained using VH GMF at the edges of sub-swaths is improved.

The edge segmentation of grains in thin-section petrographic images utilising extinction consistency perception network

The first step in petrographic image analysis is to segment the mineral grains in the thin section petrographic image, allowing the petrographer to identify the rock based on the grain size and composition. The purpose of this research is to present an automated computer vision solution for obtaining accurate and efficient edge segmentation maps. The proposed model, termed the extinction consistency perception network, is made up of three sections. The multi-angle extinction consistency block, which is based on the extinction consistency of mineral grains, uses consecutive petrographic images to generate edge-enhanced features. Then they are processed by the multi-scale edge perception network to obtain rich expression of edge features at different levels. Afterward, a distance-map penalized compound loss function is introduced to guide the model to pay more attention to grains’ edges. The generated cross-polarized petrographic image dataset (CPPID) with meticulous annotations has been shared with the community. Experimental findings show that the proposed model is effective, which is evaluated on CPPID and scores 0.940 ODS and 0.941 OIS, outperforming seven classic edge detection models by a large margin.

Underwater active polarization descattering based on a single polarized image.

Active polarization imaging techniques have tremendous potential for a variety of underwater applications. However, multiple polarization images as input are necessary for almost all methods, thereby limiting the range of applicable scenarios. In this paper, via taking full advantage of the polarization feature of target reflective light, the cross-polarized backscatter image is reconstructed via introducing an exponential function for the first time, only based on mapping relations of co-polarized image. Compared with rotating the polarizer, the result performs a more uniform and continuous distribution of grayscale. Furthermore, the relationship of degree of polarization (DOP) between the whole scene and backscattered light is established. This leads to an accurate estimation of backscattered noise and high-contrast restored images. Besides, single-input greatly simplifies the experimental process and upgrades efficiency. Experimental results demonstrate the advancement of the proposed method for objects with high polarization under various turbidities.

Influence of bipyridine isomers and some structurally related compounds on nematic liquid crystal orientation at the aqueous interface

In this paper, orientational anchoring transition of nematic liquid crystal (LC) 4-cyano-4′-pentylbiphenyl (5CB) at the LC-aqueous interface in the presence of bipyridine isomers (2,2′-bipyridine (BPY22) and 4,4′-bipyridine (BPY44)) and some structurally related compounds (1,10-phenanthroline (PANTH110) and aldrithiol-4 (ALD4)) in the aqueous phase is presented. The addition of BPY22, BPY44 and PANTH110 into the aqueous phase causes 5CB to undergo concentration dependent planar-to-homeotropic transition at the LC-aqueous interface. In contrast, ALD4 on addition into the aqueous phase depending on its concentration diminishes the interface between 5CB and the aqueous phase. Furthermore, the LC ordering transitions are related to pH of the solutions containing BPY22 or BPY44 or PANTH110 as revealed by the cross-polarized optical images and the changes in the tilt angle. Density functional theory (DFT) has been used to evaluate stabilization energy and hydrogen bonding strength for understanding the mechanism of LC ordering at the aqueous interface. Our findings provide a new set of compounds that cause homeotropic alignment of 5CB at the LC-aqueous interface, which is a prerequisite for the development LC based biosensors.

Space-Based Sub-THz ISAR for Space Situational Awareness - Laboratory Validation

The advantages of sub-terahertz technology (sub-THz, 200–700 GHz) have already been advanced for imaging and recognition of a space object's state from a space-based sensor using inverse synthetic aperture radar (ISAR). The technology benefits from wide absolute signal bandwidths, giving high range resolution and from enhanced sensitivity to surface texture. An experimental validation of such a system in controlled laboratory conditions is presented. Possible image formation methods are proposed and evaluated experimentally. Images of real parts of satellites have been produced at sub-THz frequencies and compared to lower frequency images. Other results include sub-THz bistatic ISAR, and sub-THz cross-polarized images which highlight the scattering from rough surfaces while attenuating the very bright scatterers seen from conventional monostatic images.

Towards an Algorithm for Retrieval of the Parameters of the Marine Atmospheric Boundary Layer at High Wind Speeds Using Collocated Aircraft and Satellite Remote Sensing

A method has been developed for the retrieval of the atmospheric boundary layer parameters in tropical cyclones, namely the dynamic speed, the wind speed at a 10 m height, and the roughness parameter. For the analysis, the wind speed profiles were obtained from NOAA GPS-dropsondes and collocated with the data from the Stepped-Frequency Microwave Radiometer (SFMR). The parameters of the atmospheric boundary layer from the GPS-dropsonde data were obtained by taking into account the self-similarity of the velocity defect profile. The emissivity, determined from the radiometric measurement data, was calibrated to the field data from the GPS-dropsondes. Empirical relations between the wind speed, dynamic wind speed, and aerodynamic drag coefficient with the surface emissivity have been proposed. Based on a comparison of the measured dynamic parameters and the surface emissivity, empirical formulas have also been proposed. From an analysis of cross-polarized Sentinel-1 SAR images and collocated SFMR measurements for hurricanes Irma (2017/09/07) and Maria (2017/09/21 and 2017/09/23), we have obtained the dependences of the NRCS on the ocean surface emissivity, surface wind speed, and friction velocity. These results could potentially be used to improve the algorithm for the retrieval of boundary layer parameters in tropical cyclones from remote sensing data.

Profiling of Oral Cells Morphology by means of a novel Label-Free Method of Polarization Diffraction Imaging

Currently, all oral leukoplakias, per gold standard, should undergo microscopic examination for diagnosis, which is an invasive procedure, and many patients are resistant to it due to cost, fear, or aversion to pain. We plan to develop a non-invasive assay, by using polarization diffraction imaging flow cytometry (p-DIFC) which is a technique that renders a three-dimensional model of single cells. That acquires a cross-polarized diffraction image pair (p-DI) of the coherent light scattered by a single cell with high image contrast excited by a linearly polarized laser beam. This method of single-cell assay enables extraction of cell morphology related information from the diffraction patterns embedded in the p-DI pair for each individual cell. In addition, the team has developed an optical model of cells based on confocal imaging to simulate the coherent light scattering process of a cell and convert the spatial distribution of scattered light into calculated p-DI pairs. Objectives: We aim to develop a protocol for acquiring oral mucosal cells for confocal imaging and establishing their individual three-dimensional (3D) morphologic profiles in the form of a preliminary database. In Phase 2, we will establish correlations between diffraction patterns from calculated p-DI data and the morphologic features revealed by mi- croscopy. Methods: We are collecting and quantifying individual cells from oral mucosa, in order to set a quantitative 3D morphologic single-cell parameter by confocal microscopy, for modeling study of diffraction patterns. Phase 2 of the study will involve single-cell analysis using p-DIFC for identification of early malignant changes in cytological sampling of mucosal lesions suspicious for malignancy. Conclusions: The results of this ongoing investigation will lay the foundation for future non-invasive, label-free detection of precancerous cells during intraoral examination, by investigating the profiling of cells harvested from patients in our institutions.

Evaluating a new method of remote sensing for flood mapping in the urban and peri-urban areas: Applied to Addis Ababa and the Akaki catchment in Ethiopia

The Sentinel-1 SAR dataset provides the opportunity to monitor floods at unprecedentedly high spatial and temporal resolutions. However, the accuracy of the flood maps can be affected by the image polarization, the flood detection method used, and the reference data. This research compared change detection and histogram thresholding methods using co-polarization (VV) and cross-polarization (VH) images for flood mapping in the Akaki catchment, Ethiopia, where Addis Ababa city is located. Reference data for the accuracy assessment were collected on the satellite overpass date. A new method, Root of Normalized Image Difference (RNID), has been developed for change detection. Multi-temporal flood maps using the best performing method and image polarization were generated from April to November of 2017–2020. Better accuracy was observed when using the RNID method on the VH polarization image with an overall accuracy of 95% and a kappa coefficient of 0.86. Results showed that flooding in the Akaki commonly begins in May and recedes in November, but flooding was most frequent and widespread from June to September. Irrigated land and built-up area accounted for 1057 ​ha and 544 ​ha of the inundated area, respectively. Several major roads in the study area were also affected by the floods during this period. Our findings indicate that the S-1 images were very useful for flood inundation mapping, the new change detection method (RNID) performed better in urban and peri-urban flood mapping, but the accuracy of the flood map significantly varied with the flood detection method and the image polarization.

Three-Dimensional Convolutional Neural Network on Multi-Temporal Synthetic Aperture Radar Images for Urban Flood Potential Mapping in Jakarta

Flooding in urban areas is counted as a significant disaster that must be correctly mitigated due to the huge amount of affected people, material losses, hampered economic activity, and flood-related diseases. One of the technologies available for disaster mitigation and prevention is satellites providing image data on previously flooded areas. In most cases, floods occur in conjunction with heavy rain. Thus, from a satellite’s optical sensor, the flood area is mostly covered with clouds which indicates ineffective observation. One solution to this problem is to use Synthetic Aperture Radar (SAR) sensors by observing backscatter differences before and after flood events. This research proposes mapping the flood-prone areas using machine learning to classify the areas using the 3D CNN method. The method was applied on a combination of co-polarized and cross-polarized SAR multi-temporal image datasets covering Jakarta City and the coastal area of Bekasi Regency. Testing with multiple combinations of training/testing data proportion split and a different number of epochs gave the optimum performance at an 80/20 split with 150 epochs achieving an overall accuracy of 0.71 after training in 283 min.

Analysis of polarized diffraction images of human red blood cells: a numerical study.

We carried out a systematic study on cross-polarized diffraction image (p-DI) pairs of 3098 mature red blood cells (RBCs) using optical cell models with varied morphology, refractive index (RI), and orientation. The influence of cell rotation on texture features of p-DI pairs characterized by the gray-level co-occurrence matrix (GLCM) algorithm was quantitatively analyzed. Correlations between the transverse diameters of RBCs with different RI values and scattering efficiency ratios of s- and p-polarized light were also investigated. The correlations remain strong even for RBCs with significant orientation variations. In addition, we applied a minimum redundancy maximum relevance (mRMR) algorithm to improve the performance of support vector machine (SVM) classifiers. It was demonstrated that a set of selected GLCM parameters allowed for an efficient solution of classification problems of RBCs based on morphology. For 1598 RBCs with varied shapes corresponding to normal or pathological cases, the accuracy of the SVM based classifications increased from 83.8% to 96.8% with the aid of mRMR. These results indicate the strong potential of p-DI data for rapid and accurate screening examinations of RGC shapes in routine clinical tests.

Deep Learning of Morphologic Correlations To Accurately Classify CD4+ and CD8+ T Cells by Diffraction Imaging Flow Cytometry.

The two major subtypes of human T cells, CD4+ and CD8+, play important roles in adaptive immune response by their diverse functions. To understand the structure-function relation at the single cell level, we isolated 2483 CD4+ and 2450 CD8+ T cells from fresh human splenocytes by immunofluorescent sorting and investigated their morphologic relations to the surface CD markers by acquisition and analysis of cross-polarized diffraction image (p-DI) pairs. A deep neural network of DINet-R has been built to extract 2560 features across multiple pixel scales of a p-DI pair per imaged cell. We have developed a novel algorithm to form a matrix of Pearson correlation coefficients by these features for selection of a support cell set with strong morphologic correlation in each subtype. The p-DI pairs of support cells exhibit significant pattern differences between the two subtypes defined by CD markers. To explore the relation between p-DI features and CD markers, we divided each subtype into two groups of A and B using the two support cell sets. The A groups comprise 90.2% of the imaged T cells and classification of them by DINet-R yields an accuracy of 97.3 ± 0.40% between the two subtypes. Analysis of depolarization ratios further reveals the significant differences in molecular polarizability between the two subtypes. These results prove the existence of a strong structure-function relation for the two major T cell subtypes and demonstrate the potential of diffraction imaging flow cytometry for accurate and label-free classification of T cell subtypes.

Estimating Noise Floor in Sentinel-1 Images With Linear Programming and Least Squares

Sentinel-1 is a synthetic aperture radar platform that provides free and open-source images of the Earth. A product type of Sentinel-1 is ground range detected (GRD), which records intensity while discarding phase information from the radar backscatter. Especially in cross-polarized GRD images, there are noticeable intensity changes throughout the image that are caused by amplifying the noise floor of the signal, which varies due to the nonuniform radiation pattern of the satellite's antenna. While Sentinel-1 has instrument processing facility (IPF) software to estimate the noise floor, even in the newer versions (3.1 or above) of the IPF software there are still instances where the estimates provided do not fit the actual noise floor in the image, which is particularly noticeable in transitions between adjacent subswaths. In this work, we propose a method that reduces the impact of the varying noise-floor throughout the image. The method models the intensity of the noise floor to be a power function of the radiation pattern power. The method divides the swath into several sections depending on the location of the local minimum and maximum of the radiation pattern power with respect to the range. The parameter estimation is portrayed as a geometric programming problem that is transformed into a linear programming problem by logarithmic transformation. Affine offsets are computed for each subswath by a weighted least squares approach. Vast improvement is found on extra-wide (EW) and interferometric wide (IW) Sentinel-1 modes over cross-polarized images. Code implementation is available at https://github.com/PeterQLee/sentinel1_denoise_rs.

Mapping Floods in Urban Areas From Dual-Polarization InSAR Coherence Data

Previous studies have shown that the decrease of temporal interferometric synthetic aperture radar (InSAR) coherence could be exploited to detect the appearance of floodwater in urban areas. However, as of today, approaches based on this principle only make use of single co-polarization images for identifying the presence of floodwater in the double-bounce feature. In this study, we take advantage of both co- and cross-polarization images to detect significant decreases of the multitemporal InSAR coherence in order to enhance the mapping of floodwater in urban areas. We consider that not only double-bounce scattering, but also multiple-bounce may occur in urban areas depending on how the building facades are oriented with respect to the synthetic aperture radar (SAR) sensor’s line of sight. The Sentinel-1 (S-1) mission is particularly well suited for applying and testing this kind of approach due to the systematic availability of dual-polarization data. Using as a test case, the widespread flooding in the city of Houston, USA, caused by Hurricane Harvey in 2017, we demonstrate that the proposed methodology leads to an increase of the accuracy of the urban flood maps from 75.2% when only using the VV polarization, to 82.9% when using the dual polarization information.

Siamese Adversarial Network for image classification of heavy mineral grains

The identification of heavy mineral grains based on microscopic images can significantly reduce the time and economic cost of the identification. There are several deep learning models to realize end-to-end identification of mineral image recently. However, due to the variety and complexity of mineral images, the existing models are difficult to accurately recognize heavy mineral grains in microscopic images. Here we propose the Siamese Adversarial Network (SAN) for image classification of the heavy mineral grains, which is the first time to focus on addressing the domain difference of heavy mineral images from different basins. In more details, we design a Siamese feature encoder to extract features of both the plane-polarized and cross-polarized images as internal representation of heavy mineral grains. The features are reconstructed to discard domain-related information by adversarial training the heavy mineral classifier and domain discriminator. The identification performance of the models under the three mixed domain experiments is consistently higher than the performance under the same domain settings respectively which shows that the model we proposed achieves a great generalization ability on unseen domains.

Analyzing Sea Surface Wind Distribution Characteristics of Tropical Cyclone Based on Sentinel-1 SAR Images

The spaceborne synthetic aperture radar (SAR) cross-polarization signal remains sensitive to sea surface wind speed with high signal-to-noise ratio under tropical cyclone (TC) conditions. It has the capability of observing TC intensity and size information over the ocean with large coverage and high spatial resolution. In this paper, TC wind distribution characteristics were studied based on SAR images. We collected 41 Sentinel-1A/B cross-polarization images covering TC eye, which were acquired between 2016 and 2020. For each case, sea surface wind speeds were retrieved by the modified MS1A model in a spatial resolution of 1 km. After deriving the value and location of maximum wind speed, wind fields were simulated symmetrically within a 200 km radius. Two new methodologies were proposed to calculate the decay index and the symmetry index based on the retrieved and simulated wind fields. Characteristics of the two indices were analyzed with respect to maximum wind. In addition, the maximum and averaged wind speeds of the right, back and left side of the motion direction were compared with TC intensity and storm motion speed. Statistical results indicate that right-side wind speed is the strongest for maximum and average, the wind difference between the left and right side is dependent on storm motion speed.

Late Changes in the Extracellular Matrix of the Bladder after Radiation Therapy for Pelvic Tumors.

Radiation therapy is one of the cardinal approaches in the treatment of malignant tumors of the pelvis. It leads to the development of radiation-induced complications in the normal tissues. Thus, the evaluation of radiation-induced changes in the extracellular matrix of the normal tissue is deemed urgent, since connective tissue stroma degradation plays a crucial role in the development of Grade 3–4 adverse effects (hemorrhage, necrosis, and fistula). Such adverse effects not only drastically reduce the patients’ quality of life but can also become life-threatening. The aim of this study is to quantitatively analyze the bladder collagen state in patients who underwent radiation therapy for cervical and endometrial cancer and in patients with chronic bacterial cystitis and compare them to the normal bladder extracellular matrix. Materials and methods: One hundred and five patients with Grade 2–4 of radiation cystitis, 67 patients with bacterial chronic cystitis, and 20 volunteers without bladder pathology were enrolled. Collagen changes were evaluated depending on its hierarchical level: fibrils and fibers level by atomic force microscopy; fibers and bundles level by two-photon microscopy in the second harmonic generation (SHG) mode; general collagen architectonics by cross-polarization optical coherence tomography (CP OCT). Results: The main sign of the radiation-induced damage of collagen fibrils and fibers was the loss of the ordered “basket-weave” packing and a significant increase in the total area of ruptures deeper than 1 µm compared to the intact sample. The numerical analysis of SHG images detected that a decrease in the SHG signal intensity of collagen is correlated with the increase in the grade of radiation cystitis. The OCT signal brightness in cross-polarization images demonstrated a gradual decrease compared to the intact bladder depending on the grade of the adverse event. Conclusions: The observed correspondence between the extracellular matrix changes at the microscopic level and at the level of the general organ architectonics allows for the consideration of CP OCT as a method of “optical biopsy” in the grading of radiation-induced collagen damage.