Distinguishing structural features between Crohn's disease and gastrointestinal luminal tuberculosis using Mueller matrix derived parameters

Mueller matrix polarimetry for quantitative evaluation of the Achilles tendon injury recovery

Achilles tendon injuries, as a widely existing disease, have attracted a lot of research interest. Mueller matrix polarimetry, as a novel label-free quantitative imaging method, has been widely used in various applications of lesion identification and pathological diagnosis. However, focusing on the recovery process of Achilles tendon injuries, current optical imaging methods have not yet achieved the label-free precise identification and quantitative evaluation. In this study, using Mueller matrix polarimetry, various Achilles tendon injury samples were characterized specifically, and the efficacy of different recovery schemes was evaluated accordingly. Experiments indicate that injured Achilles tendons show less phase retardance, larger diattenuation, and relatively disordered orientation. The combination of experiments with Monte Carlo simulation results illustrate the microscopic mechanism of the Achilles tendon recovery process from three aspects, that is, the increased fiber diameter, a more consistent fiber orientation, and greater birefringence induced by more collagen protein. Finally, based on the statistical distribution of polarization measurements, a polarization specific characterization parameter was extracted to construct a label-free image, which cannot only intuitively show the injury and recovery of Achilles tendon samples, but also give a quantitative evaluation of the treatment.

Full-Automatic High-Efficiency Mueller Matrix Microscopy Imaging for Tissue Microarray Inspection

This paper proposes a full-automatic high-efficiency Mueller matrix microscopic imaging (MMMI) system based on the tissue microarray (TMA) for cancer inspection for the first time. By performing a polar decomposition on the sample's Mueller matrix (MM) obtained by a transmissive MMMI system we established, the linear phase retardance equivalent waveplate fast-axis azimuth and the linear phase retardance are obtained for distinguishing the cancerous tissues from the normal ones based on the differences in their polarization characteristics, where three analyses methods including statistical analysis, the gray-level co-occurrence matrix analysis (GLCM) and the Tamura image processing method (TIPM) are used. Previous MMMI medical diagnostics typically utilized discrete slices for inspection under a high-magnification objective (20×-50×) with a small field of view, while we use the TMA under a low-magnification objective (5×) with a large field of view. Experimental results indicate that MMMI based on TMA can effectively analyze the pathological variations in biological tissues, inspect cancerous cervical tissues, and thus contribute to the diagnosis of postoperative cancer biopsies. Such an inspection method, using a large number of samples within a TMA, is beneficial for obtaining consistent findings and good reproducibility.

Discrepancy of coordinate system selection in backscattering Mueller matrix polarimetry: exploring photon coordinate system transformation invariants

In biomedical studies, Mueller matrix polarimetry is gaining increasing attention because it can comprehensively characterize polarization-related vectorial properties of the sample, which are crucial for microstructural identification and evaluation. For backscattering Mueller matrix polarimetry, there are two photon coordinate selection conventions, which can affect the following Mueller matrix parameters calculation and information acquisition quantitatively. In this study, we systematically analyze the influence of photon coordinate system selection on the backscattering Mueller matrix polarimetry. We compare the Mueller matrix elements in the right-handed-nonunitary and non-right-handed-unitary coordinate systems, and specifically deduce the changes of Mueller matrix polar decomposition, Mueller matrix Cloude decomposition and Mueller matrix transformation parameters widely used in backscattering Mueller matrix imaging as the photon coordinate system varied. Based on the theoretical analysis and phantom experiments, we provide a group of photon coordinate system transformation invariants for backscattering Mueller matrix polarimetry. The findings presented in this study give a crucial criterion of parameters selection for backscattering Mueller matrix imaging under different photon coordinate systems.

Analyzing the Influence of Imaging Resolution on Polarization Properties of Scattering Media Obtained From Mueller Matrix

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.

Deep learning for denoising in a Mueller matrix microscope

The Mueller matrix microscope is a powerful tool for characterizing the microstructural features of a complex biological sample. Performance of a Mueller matrix microscope usually relies on two major specifications: measurement accuracy and acquisition time, which may conflict with each other but both contribute to the complexity and expenses of the apparatus. In this paper, we report a learning-based method to improve both specifications of a Mueller matrix microscope using a rotating polarizer and a rotating waveplate polarization state generator. Low noise data from long acquisition time are used as the ground truth. A modified U-Net structured network incorporating channel attention effectively reduces the noise in lower quality Mueller matrix images obtained with much shorter acquisition time. The experimental results show that using high quality Mueller matrix data as ground truth, such a learning-based method can achieve both high measurement accuracy and short acquisition time in polarization imaging.

Polarization Aberrations in High-Numerical-Aperture Lens Systems and Their Effects on Vectorial-Information Sensing

The importance of polarization aberrations has been recognized and studied in numerous optical systems and related applications. It is known that polarization aberrations are particularly crucial in certain photogrammetry and microscopy techniques that are related to vectorial information—such as polarization imaging, stimulated emission depletion microscopy, and structured illumination microscopy. Hence, a reduction in polarization aberrations would be beneficial to different types of optical imaging/sensing techniques with enhanced vectorial information. In this work, we first analyzed the intrinsic polarization aberrations induced by a high-NA lens theoretically and experimentally. The aberrations of depolarization, diattenuation, and linear retardance were studied in detail using the Mueller matrix polar-decomposition method. Based on an analysis of the results, we proposed strategies to compensate the polarization aberrations induced by high-NA lenses for hardware-based solutions. The preliminary imaging results obtained using a Mueller matrix polarimeter equipped with multiple coated aspheric lenses for polarization-aberration reduction confirmed that the conclusions and strategies proposed in this study had the potential to provide more precise polarization information of the targets for applications spanning across classical optics, remote sensing, biomedical imaging, photogrammetry, and vectorial optical-information extraction.

Effect of the Samples' Surface With Complex Microscopic Geometry on 3 × 3 Mueller Matrix Measurement of Tissue Bulks

The clinical in vivo tissue bulks' surface is always coarse and shows a complex microscopic geometry which may affect the visual effect of polarization images and calculation of polarization parameters of the sample. To confirm whether this effect would cause identification difficulties and misjudgments on the target recognition when performed the polarization imaging based on 3 × 3 Mueller matrix measurement, cylindrical type and slope type physical models were used to study and analyze the effect of the surface with complex microscopic geometry on the polarization images. Then, clinical tumor bulk samples were used to interact with different sizes of patterns to simulate the different complex microscopic geometry and test the coarse surface effect on polarization images. Meanwhile, assessment parameters were defined to evaluate and confirm the variation between two polarization images quantitatively. The results showed that the polarization imaging of the sample surface with the complex microscopic geometry led to acceptable visual effect and limited quantitative variation on the value of polarization parameters and assessment parameters, and it caused no identification difficulties on target recognition, indicating that it is feasible to apply the polarization imaging based on 3 × 3 Mueller matrix measurement on clinical in vivo tissues with the complex microscopic geometry sample surface.

A Polarization-Imaging-Based Machine Learning Framework for Quantitative Pathological Diagnosis of Cervical Precancerous Lesions

Polarization images encode high resolution microstructural information even at low resolution. We propose a framework combining polarization imaging and traditional microscopy imaging, constructing a dual-modality machine learning framework that is not only accurate but also generalizable and interpretable. We demonstrate the viability of our proposed framework using the cervical intraepithelial neoplasia grading task, providing a polarimetry feature parameter to quantitatively characterize microstructural variations with lesion progression in hematoxylin-eosin-stained pathological sections of cervical precancerous tissues. By taking advantages of polarization imaging techniques and machine learning methods, the model enables interpretable and quantitative diagnosis of cervical precancerous lesion cases with improved sensitivity and accuracy in a low-resolution and wide-field system. The proposed framework applies routine image-analysis technology to identify the macro-structure and segment the target region in H&E-stained pathological images, and then employs emerging polarization method to extract the micro-structure information of the target region, which intends to expand the boundary of the current image-heavy digital pathology, bringing new possibilities for quantitative medical diagnosis.

Assessment of tissue pathology using optical polarimetry

Optical polarimetry have been extensively used for the non-invasive assessment of biological tissues. However, the knowledge regarding differences in polarimetric signatures of different tissue pathologies is very scattered, confounding the deduction of a global trend of the polarimetric variables for healthy and pathological tissues. The purpose of this study was to bridge this gap. We conducted a rigorous online survey to collect all published studies that report the two most common polarimetric variables (i.e., depolarization and retardance) for any type of tissue pathology. A total of 101 studies describing the polarimetric assessment of tissues were collected, wherein 253 (i.e., n_human = 149, n_animal = 104) different type of tissues were optically characterized. Most tissue samples (172/253) were investigated in ex vivo settings. The data showed 32 different types of tissues pathologies, where the most common pathology was cancer and its subtypes. The skin tissues were the most frequently explored tissues, followed by tissue samples from breast, colon, liver, and cervix. Although differences in polarimetric signatures of different tissue pathologies were summarized from the included studies, generalization of the results was hindered by the presentation of polarimetric data in a non-uniform format. The analyses presented in this study may provide an important reference for future polarimetric studies that conduct optical assessment of tissues at greater depth, particularly in the context of optical biopsy/digital staining.

Isolating individual polarization effects from the Mueller matrix: comparison of two non-decomposition techniques

The prevailing formalisms for isolating individual polarization effects from the experimental Mueller matrix M can be broadly divided into two categories; decomposition of M to derive the individual optical effects and directly associating the individual optical effects to specific elements of M (i.e., non-decomposition techniques). Mueller matrix transformation (MMT) and direct interpretation of Mueller matrix (DIMM) are two popular techniques of the latter category. In this study, these two non-decomposition techniques (i.e., MMT and DIMM) are compared in a detailed quantitative analysis comprising of tissues (n = 53) and phantom (n = 45) samples. In particular, two commonly investigated polarimetric variables (i.e., depolarization and retardance) were calculated from the experimentally measured M using both the non-decomposition (i.e., MMT and DIMM) techniques. The comparison carried out with scatter plots (integrated with the correlation coefficients), violin plots and Bland and Altman plots revealed better agreement of depolarization-related variables (as compared to the retardance) between the two non-decomposition techniques. The comparative analyses presented here would be beneficial for the interpretation of polarimetric variables and optical characterization of turbid media.

Comparison between image texture and polarization features in histopathology

Digital pathology has shown great importance for diagnostic purposes in the digital age by integrating basic image features into multi-modality information. We quantify the degree of correlation between the multiple texture features from H&E images and polarization parameter sets derived from Mueller matrix images of the same sample to provide more microstructural information for assisting diagnosis. The experimental result shows the correlations between texture feature and polarization parameter via Pearson coefficients. Polarization parameters t₁ , D_L and the depolarization parameter Δ correlated with image texture features Tamura_Fcon and Tamura_Frgh, and can be used as powerful tools to quantitatively characterize cell nuclei related with tumor progression in breast pathological tissues. Polarization parameters δ and r_L associated with the image texture feature Tamura_Flin have great potential for the quantitative characterization of proliferative fibers produced by inflammation. Furthermore, polarization parameters have the advantages of stable recognition in low resolution images. This work validates the associations between image texture features and polarization parameters and the merit of polarization imaging methods in low-resolution situations.

Deriving Polarimetry Feature Parameters to Characterize Microstructural Features in Histological Sections of Breast Tissues

OBJECTIVE

Mueller matrix polarimetry technique has been regarded as a powerful tool for probing the microstructural information of tissues. The multiplying of cells and remodeling of collagen fibers in breast carcinoma tissues have been reported to be related to patient survival and prognosis, and they give rise to observable patterns in hematoxylin and eosin (H&E) sections of typical breast tissues (TBTs) that the pathologist can label as three distinctive pathological features (DPFs)-cell nuclei, aligned collagen, and disorganized collagen. The aim of this paper is to propose a pixel-based extraction approach of polarimetry feature parameters (PFPs) using a linear discriminant analysis (LDA) classifier. These parameters provide quantitative characterization of the three DPFs in four types of TBTs.

METHODS

The LDA-based training method learns to find the most simplified linear combination from polarimetry basis parameters (PBPs) constrained under the accuracy remains constant to characterize the specific microstructural feature quantitatively in TBTs.

RESULTS

We present results from a cohort of 32 clinical patients with analysis of 224 regions-of-interest. The characterization accuracy for PFPs ranges from 0.82 to 0.91.

CONCLUSION

This work demonstrates the ability of PFPs to quantitatively characterize the DPFs in the H&E pathological sections of TBTs.

SIGNIFICANCE

This technique paves the way for automatic and quantitative evaluation of specific microstructural features in histopathological digitalization and computer-aided diagnosis.

Comparative study of the influence of imaging resolution on linear retardance parameters derived from the Mueller matrix

Polarization imaging techniques are emerging tools to provide quantitative information of anisotropic structures, such as the density and orientation distribution of fibers in tissue samples. Recently, it is found that when using Mueller matrix polarimetry to obtain the structural features of tissue samples, some information can be revealed by relatively low-resolution polarization parameter images. Thus, to analyze what kinds of anisotropic optical and structural information contained in high-resolution polarization images are preserved in low-resolution ones, here we carry out a comparative study of the influence of imaging resolution on the Mueller matrix derived linear retardance parameters. We measure the microscopic Mueller matrix of human healthy breast duct tissues and ductal carcinoma in situ (DCIS) tissues, which have distinct typical fibrous structures, using objectives with different numerical aperture. Then we quantitatively compare a group of image texture feature parameters of the linear retardance parameters images under high and low imaging resolutions. The results demonstrate that the fibers density information contained in the texture features of linear retardance δ parameter image are preserved well with the decline of imaging resolution. While for the azimuthal orientation parameter θ which closely related to the spatial location, we still need high imaging resolution to obtain quantitative structural information. The study provides an important criterion to decide which information of fibrous structures can be extracted accurately using transmission Mueller matrix microscope with low numerical aperture objectives.

Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography

Optical coherence tomography (OCT) has shown potential in differentiating normal colonic mucosa from neoplasia. In this study of 33 fresh human colon specimens, we report the first use of texture features and computer vision-based imaging features acquired from en face scattering coefficient maps to characterize colorectal tissue. En face scattering coefficient maps were generated automatically using a new fast integral imaging algorithm. From these maps, a gray-level cooccurrence matrix algorithm was used to extract texture features, and a scale-invariant feature transform algorithm was used to derive novel computer vision-based features. In total, 25 features were obtained, and the importance of each feature in diagnosis was evaluated using a random forest model. Two classifiers were assessed on two different classification tasks. A support vector machine model was found to be optimal for distinguishing normal from abnormal tissue, with 94.7% sensitivity and 94.0% specificity, while a random forest model performed optimally in further differentiating abnormal tissues (i.e., cancerous tissue and adenomatous polyp) with 86.9% sensitivity and 85.0% specificity. These results demonstrated the potential of using OCT to aid the diagnosis of human colorectal disease.

Probing variations of fibrous structures during the development of breast ductal carcinoma tissues via Mueller matrix imaging

Recently, we developed a label-free method to probe the microstructural information and optical properties of unstained thin tissue slices based on microscopic Mueller matrix imaging technique. In this paper, we take the microscopic Mueller matrix images of human breast ductal carcinoma tissue samples at different pathological stages, and then calculate and analyze their retardance-related Mueller matrix-derived parameters. To reveal the microstructural features more quantitatively and precisely, we propose a new method based on first-order statistical properties of image to transform the 2D images of Mueller matrix parameters into several statistical feature vectors. We evaluate each statistical feature vector by corresponding classification characteristic value extracted from the statistical features of Mueller matrix parameters images of healthy breast duct tissue samples. The experimental results indicate that these statistical feature vectors of Mueller matrix derived parameters may become powerful tools to quantitatively characterize breast ductal carcinoma tissue samples at different pathological stages. It has the potential to facilitate automating the staging process of breast ductal carcinoma tissue, resulting in the improvement of diagnostic efficiency.