Deep learning Mueller matrix feature retrieval from a snapshot Stokes image

Si, Lu; Huang, Tongyu; Wang, Xingjian; Ya, Yao; Dong, Yonggui; Ma, Hui

doi:10.1364/oe.451612

Cited by 13 publications

(9 citation statements)

References 46 publications

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“…Certain anatomical structures are for example sensitive to polarization-dependent attenuation as in Diattenuation Imaging [80]. For a complete picture, multi-modal imaging in form of combining 3D-PLI with Müller [81] and Stokes polarimetry [82] can also be taken into account.…”

Section: Discussionmentioning

confidence: 99%

Fast data-driven computation and intuitive visualization of fiber orientation uncertainty in 3D-polarized light imaging

et al. 2022

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In recent years, the microscopy technology referred to as Polarized Light Imaging (3D-PLI) has successfully been established to study the brain’s nerve fiber architecture at the micrometer scale. The myelinated axons of the nervous tissue introduce optical birefringence that can be used to contrast nerve fibers and their tracts from each other. Beyond the generation of contrast, 3D-PLI renders the estimation of local fiber orientations possible. To do so, unstained histological brain sections of 70 μm thickness cut at a cryo-microtome were scanned in a polarimetric setup using rotating polarizing filter elements while keeping the sample unmoved. To address the fundamental question of brain connectivity, i. e., revealing the detailed organizational principles of the brain’s intricate neural networks, the tracing of fiber structures across volumes has to be performed at the microscale. This requires a sound basis for describing the in-plane and out-of-plane orientations of each potential fiber (axis) in each voxel, including information about the confidence level (uncertainty) of the orientation estimates. By this means, complex fiber constellations, e. g., at the white matter to gray matter transition zones or brain regions with low myelination (i. e., low birefringence signal), as can be found in the cerebral cortex, become quantifiable in a reliable manner. Unfortunately, this uncertainty information comes with the high computational price of their underlying Monte-Carlo sampling methods and the lack of a proper visualization. In the presented work, we propose a supervised machine learning approach to estimate the uncertainty of the inferred model parameters. It is shown that the parameter uncertainties strongly correlate with simple, physically explainable features derived from the signal strength. After fitting these correlations using a small sub-sample of the data, the uncertainties can be predicted for the remaining data set with high precision. This reduces the required computation time by more than two orders of magnitude. Additionally, a new visualization of the derived three-dimensional nerve fiber information, including the orientation uncertainty based on ellipsoids, is introduced. This technique makes the derived orientation uncertainty information visually interpretable.

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Section: Discussionmentioning

confidence: 99%

Fast data-driven computation and intuitive visualization of fiber orientation uncertainty in 3D-polarized light imaging

et al. 2022

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“…Specifically, the MMPD decomposes the interaction between light and medium into three main processes of polarization properties, namely diattenuation ( D ), retardation ( R ), and depolarization (Δ), as shown in Equation (2). Further, through the decomposition process, we can obtain a group of polarization parameters, among which the MMPD- D , MMPD- δ , and MMPD-Δ corresponding to dichroism, linear retardation, and depolarization are mostly used in biomedical trials [ 68 , 69 ]. The detailed polar decomposition process is shown in Equation (2)

where M is the measured Mueller matrix, M ij is the element of M ; M ∆ , M R and M D are the sub-matrices of depolarization, retardation and diattenuation, respectively, M Rij is the element of M R ; D is the diattenuation, δ is the linear retardation, Δ is the depolarization.…”

Section: Methodsmentioning

confidence: 99%

A Dual-Modality Imaging Method Based on Polarimetry and Second Harmonic Generation for Characterization and Evaluation of Skin Tissue Structures

Deng

Fan

Chen

et al. 2023

IJMS

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The characterization and evaluation of skin tissue structures are crucial for dermatological applications. Recently, Mueller matrix polarimetry and second harmonic generation microscopy have been widely used in skin tissue imaging due to their unique advantages. However, the features of layered skin tissue structures are too complicated to use a single imaging modality for achieving a comprehensive evaluation. In this study, we propose a dual-modality imaging method combining Mueller matrix polarimetry and second harmonic generation microscopy for quantitative characterization of skin tissue structures. It is demonstrated that the dual-modality method can well divide the mouse tail skin tissue specimens’ images into three layers of stratum corneum, epidermis, and dermis. Then, to quantitatively analyze the structural features of different skin layers, the gray level co-occurrence matrix is adopted to provide various evaluating parameters after the image segmentations. Finally, to quantitatively measure the structural differences between damaged and normal skin areas, an index named Q-Health is defined based on cosine similarity and the gray-level co-occurrence matrix parameters of imaging results. The experiments confirm the effectiveness of the dual-modality imaging parameters for skin tissue structure discrimination and assessment. It shows the potential of the proposed method for dermatological practices and lays the foundation for further, in-depth evaluation of the health status of human skin.

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“…The Mueller matrix polar decomposition (MMPD) method [40], proposed by Lu and Chipman, has been extensively applied in various areas of biomedical research. MMPD decomposes the Mueller matrix M into three submatrices: diattenuation (D), retardance (R), and depolarization ( ) [41], which is depicted in Equation (2). In summary, we can extract useful information related to structural properties and polarization characteristics by MMPD.…”

Section: Mueller Matrix Microscope and Mueller Matrix Polar Decomposi...mentioning

confidence: 99%

A dual-modality imaging method based on polarimetry and second harmonic generation

Shi¹,

He²,

Ma³

2023

Second Conference on Biomedical Photonics and Cross-Fusion (BPC 2023)

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Characterization and evaluation of skin tissue structures are crucial for diagnosis of skin diseases. Recently, the Mueller matrix polarimetry and second harmonic generation microscopy have been widely used in skin tissue imaging due to their unique advantages. However, it is difficult to evaluate comprehensive structural characteristics of complex layered skin tissue by a single imaging modality. Therefore, we propose a dual-modality imaging method combining Mueller matrix polarimetry and second harmonic generation microscopy to achieve the automatic differentiation and quantitative evaluation of different layers of skin tissue. It is demonstrated that the dual-modality method can well divide the mouse tail skin tissue specimens’ images into three layers of stratum corneum, epidermis, and dermis. Then, the texture feature vector extracted by gray level co-occurrence matrix is used to quantitatively characterize the structure features of each layer. Finally, we propose an index named S-Health based on cosine similarity and the gray level co-occurrence matrix texture parameters to quantitatively measure the structural differences between damaged and normal skin areas. The experiments validate the efficacy of the dual-modality imaging method in discriminating and assessing skin tissue structures. It confirms the potential of the proposed method for dermatological practices

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Deep learning Mueller matrix feature retrieval from a snapshot Stokes image

Cited by 13 publications

References 46 publications

Fast data-driven computation and intuitive visualization of fiber orientation uncertainty in 3D-polarized light imaging

Fast data-driven computation and intuitive visualization of fiber orientation uncertainty in 3D-polarized light imaging

A Dual-Modality Imaging Method Based on Polarimetry and Second Harmonic Generation for Characterization and Evaluation of Skin Tissue Structures

A dual-modality imaging method based on polarimetry and second harmonic generation

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