RGB Mueller matrix microscopy: Advantages of simultaneous polarimetric imaging at three wavelengths

We propose a cross-modality method that translates polarimetric images into bright-field. In the lung tissue histological analysis, immunohistochemical (IHC) staining of tissues is widely used to specify particular cellular events especially in precision medicine. In this work, we measured hematoxylin and eosin (HE) stained slices by Mueller matrix (MM) microscopy and then fed polarimetric data into a well-designed generative adversarial network (GAN). The network can generate images that are equivalent to the IHC stained from bright-field microscopy. This will assist pathologists with the real IHC staining procedure and pathological diagnosis. Instead of preparing specimens from scratch, we collected already existing specimens, i.e., the adjacent HE and IHC stained slices from the same tissue volume. We adopted the CycleGAN to learn the translation between unaligned images from two domains. We used a U-Net based generator and a PixelGAN based discriminator in the model. The efficacy of this method was demonstrated on smooth muscle actin (SMA) staining in lung tissue. The results are evaluated by three image quality assessment methods by comparing the generated and real staining images.

Method for Mueller matrix acquisition based on a division-of-aperture simultaneous polarimetric imaging technique

Polarization characteristics are significantly crucial for tasks in various fields, including the remote sensing of oceans and atmosphere, as well as the polarization LIDAR and polarimetric imaging in scattering media. Many polarimetric metrics (such as the degree of polarization, polarization angle diattenuation, and depolarization) have been proposed to enrich the characterization and improve the task performance in scattering media; yet, their related efficacy is limited, especially in high turbidity conditions. The indices of polarimetric purity (IPPs), including three different depolarization metrics, have been successfully applied to biomedical diagnosis. However, it is still debatable whether IPPs also are excellent metrics for identifying or distinguishing objects in scattering media. In this work, we seek to provide physical insights into the application of distinguishing and identifying different objects via IPPs. Imaging experiments are devised and performed on different objects, e.g., metals and plastics, under different turbidity levels, demonstrating the superiority of IPPs as excellent metrics for object identification in scattering conditions. The experimental results show that the IPPs images can enhance image contrast and improve discriminability, as well as break the limitation of traditional intensity-model imaging techniques when further combined with dehazing or enhancing algorithms. Importantly, as the used Mueller matrix (MM) and the related IPPs can also be obtained via other types of MM polarimeters (e.g., PolSAR and MM microscopy), the proposed solution and idea have potential for such applications as biomedical imaging, photogrammetry, and remote sensing.

Mueller matrix microscopy (MMM) is a powerful approach to probe microstructural and optical information of many important specimens (e.g., tissue and bacteria), which otherwise cannot be obtained directly from intensity or spectral images. Achieving high lateral resolution in MMM, similar to other microscopy approaches, remains a challenge. Here, we extend the idea of microsphere (MS) -assisted microscopy into MMM toward resolution-enhanced polarimetric imaging. The goal is achieved by insertion of a transparent MS in the working distance of the imaging microscope objective in the optical train of an MMM system. We experimentally show that an MS close to the sample in MMM may increase the resolution beyond the intrinsic diffraction limit of the system by redirecting the higher spatial frequencies of the sample into the acceptance cone. In order to be a case in point, the experiment is conducted on a standard holographic diffraction grating with 1 µm line-width, which is beyond the diffraction limit of a 10× objective. Two-dimensional images of the Mueller matrix and some of the widely used quantitative polarimetric parameters of the sample are calculated and compared in the two cases before and after insertion of MS. The proposed arrangement is easy to implement and has the potential to serve as a high-resolution polarimetric microscope for visualizing the polarization characteristics of the microscopic objects.

Full-field measurement of residual stress in single-crystal diamond substrates based on Mueller matrix microscopy

Mueller polarimetric microscopic images analysis based classification of breast cancer cells

The report contains a structural-logical diagram and design of studies using polarization and Mueller-matrix microscopy methods of histological sections of the brain. The results of the differential diagnosis of the formation of hemorrhages of traumatic origin, ischemic cerebral infarction and hemorrhagic genesis using Mueller-matrix microscopy are presented. <li> Azimuthally invariant Mueller-matrix images of linear birefringence (MMI LB) of histological sections of the brain and operational characteristics of the method of their statistical analysis. </li> <li> Azimuthally invariant Mueller-matrix images of circular birefringence (MMI CB) of histological sections of the brain and operational characteristics of the method of their statistical analysis </li>. <li> Differential diagnosis of the duration of the formation of hemorrhages of traumatic origin, ischemic cerebral infarction and hemorrhagic genesis using Mueller matrix mapping. </li><li> Temporal dynamics of changes in the statistical structure of maps of Mueller-matrix invariants of optical activity (MMI OA) of histological sections of the brain. </li> <li> Temporal dynamics of changes in the statistical structure of maps of Mueller-matrix invariants of linear birefringence (MMI LB) of histological sections of the brain. </li>

Recently, virtual staining techniques have attracted more and more attention, which can help bypass the chemical staining process of traditional histopathological examination, saving time and resources. Meanwhile, as an emerging tool to characterize specific tissue structures in a label-free manner, the Mueller matrix microscopy can supplement more structural information that may not be apparent in bright-field images. In this Letter, we propose the Mueller matrix guided generative adversarial networks (MMG-GAN). By integrating polarization information provided by the Mueller matrix microscopy, the MMG-GAN enables the effective transformation of input H&E-stained images into corresponding Masson trichrome (MT)-stained images. The experimental results demonstrate the accuracy of the generated images by MMG-GAN and reveal the potential for more stain transformation tasks by incorporating the Mueller matrix polarization information, laying the foundation for future polarimetry-assisted digital pathology.

Tongue cancer, the most aggressive subtype of oral cancer, presents critical challenges due to the limited number of specialists available and the time-consuming nature of conventional histopathological diagnosis. To address these issues, we developed an intelligent diagnostic system integrating Mueller matrix microscopy with deep learning to enhance diagnostic accuracy and efficiency. Through Mueller matrix polar decomposition and transformation, micro-polarization feature parameter images were extracted from tongue cancer tissues, and purity parameter images were generated by calculating the purity of the Mueller matrices. A multi-stage feature dataset of Mueller matrix parameter images was constructed using histopathological samples of tongue cancer tissues with varying stages. Based on this dataset, the clinical potential of Mueller matrix microscopy was preliminarily validated for histopathological diagnosis of tongue cancer. Four mainstream medical image classification networks—AlexNet, ResNet50, DenseNet121 and VGGNet16—were employed to quantitatively evaluate the classification performance for tongue cancer stages. DenseNet121 achieved the highest classification accuracy of 98.48%, demonstrating its potential as a robust framework for rapid and accurate intelligent diagnosis of tongue cancer.

The Mueller matrix contains abundant micro- and even nanostructural information of media. Especially, it can be used as a powerful tool to characterize anisotropic structures quantitatively, such as the particle size, density, and orientation information of fibers in the sample. Compared with unpolarized microscopic imaging techniques, Mueller matrix microscopy can also obtain some essential structural information about the sample from the derived parameters images at low resolution. Here, to analyze the comprehensive effects of imaging resolution on polarization properties obtained from the Mueller matrix, we, first, measure the microscopic Mueller matrices of unstained rat dorsal skin tissue slices rich in collagen fibers using a series of magnifications or numerical aperture (NA) values of objectives. Then, the first-order moments and image texture parameters are quantified and analyzed in conjunction with the polarization parameter images. The results show that the Mueller matrix polar decomposition parameters diattenuation D, linear retardance δ, and depolarization Δ images obtained using low NA objective retain most of the structural information of the sample and can provide fast imaging speed. In addition, the scattering phase function analysis and Monte Carlo simulation based on the cylindrical scatterers reveal that the diattenuation parameter D images with different imaging resolutions are expected to be used to distinguish among the fibrous scatterers in the medium with different particle sizes. This study provides a criterion to decide which structural information can be accurately and rapidly obtained using a transmission Mueller matrix microscope with low NA objectives to assist pathological diagnosis and other applications.

Sub-second imaging speed Mueller matrix microscopy for observing dynamic growth of banded spherulites.

In clinical medicine, a pathologist often needs to examine cells or thin slices of tissues to identify abnormalities that are markers or precursors of diseases. Various chemical and immunohistochemical staining techniques have been developed to selectively label certain components to bring up the contrasts of specific microstructures. It is well known that a Mueller matrix contains rich information on the microstructure and optical properties of a sample. Using proper data analysis techniques, Mueller matrix images can also be transformed into new polarization parameters sensitive only to specific microstructural features. These new polarization parameters can selectively enhance the contrast of specific features in images of unstained pathological slide to help identify abnormalities. In recent studies, we set up a modulus design Mueller matrix microscope by adding polarization optics components into the optical path of a commercial transmission microscope. We take multiple measurements of the unstained pathological slide at different polar and azimuth angles, then derive an intrinsic Mueller matrix (IMM) which represents only the microstructural characters of the sample without the interference by the sample orientation. Such orientation-independent IMM images preserve to the maximum extent the pathological information of the tissue samples. Using Mueller matrix decomposition and transformation techniques, we demonstrate in preliminary tests that we are able to selectively enhance different characteristic features in different cancer tissues. With the fast advances in big-data analysis techniques, it is expected that label-free Mueller matrix microscopy is a potentially powerful tool for the histopathologists to identify characteristic features in complex tissue samples.

In this study, we propose a quantitative technique to analyze and evaluate microstructures of skin hair follicles based on Mueller Matrix transmission microscopy. We measure the Mueller matrix polar decomposition (MMPD) parameter images to reveal the characteristic linear birefringence distribution induced by hair follicles in mouse skin tissue samples. The results indicate that the Mueller matrix-derived parameters can be used to reveal the location and structural integrity of hair follicles. For accurate hair follicle location identification and quantitative structural evaluations, we use the image segmentation method, sliding window algorithm, and image texture analysis methods together to process the Mueller matrix-derived images. It is demonstrated that the hair follicle regions can be more accurately recognized, and their locations can be precisely identified based on the Mueller matrix-derived texture parameters. Moreover, comparisons between manual size measurement and polarimetric calculation results confirm that the Mueller matrix parameters have good performance for follicle size estimation. The results shown in this study suggest that the technique based on Mueller matrix microscopy can realize automatically hair follicle identification, detection, and quantitative evaluation. It has great potential in skin structure-related studies and clinical dermatological applications.

Mueller matrix (MM) polarimetry can provide comprehensive information about the polarization properties that are closely related to the microstructural features and has demonstrated its potential in biomedical studies and clinical practices, and bright-field microscopy is widely used in pathological diagnosis as the golden standard. In this work, we improve the throughput of MM microscopy by learning a statistical transformation between these two imaging systems based on deep learning. Using this approach, the MM microscope can generate an image that is equivalent to a bright-field microscope image of the matching field of view. We add new transformative capability to the existing MM imaging system without requiring extra hardware. The translation model is based on conditional generative adversarial network with customized loss functions. We demonstrated the effectiveness of our approach on liver and breast tissues and evaluated the performance by four quantitative similarity assessment methods in pixel, image and distribution levels, respectively.

Diagnosing classical and hypermobile Ehlers-Danlos syndromes, cEDS and hEDS, respectively, remains challenging due to overlapping clinical presentations and the lack of objective diagnostic tools. A label-free quantitative method for detecting EDS from normal tissues and for distinguishing its subtypes could support more timely and accurate clinical evaluation. We evaluate the capability of Mueller matrix (MM) polarized light microscopy to resolve polarimetric contrasts between healthy and EDS samples and to assess its potential for differentiating between the cEDS and hEDS subtypes. Unstained skin biopsy slides from 19 participants (healthy, ; cEDS, ; hEDS, ) were imaged using a custom MM microscopy setup. In addition to acquiring 16 MM elements, 24 polarimetric parameters were obtained and tested for their detection and differentiation abilities. Group-wise comparisons of median parameter values were performed after assessing distribution normality using the Shapiro-Wilk test, applying either the Mann-Whitney U test or the independent-samples -test as appropriate. Three polarimetric parameters ( , , and ) showed statistically significant differences between EDS-affected and healthy skin, with higher values observed in healthy tissue, consistent with more uniform collagen alignment in healthy subjects. Five parameters ( , , , , and ) significantly differentiated cEDS from hEDS, with demonstrating the strongest separation. was the only parameter to show statistical significance for both tasks. MM polarimetry shows promise for label-free, quantitative assessment of skin collagen organization and offers potential for detecting EDS and differentiating its subtypes.