Wide-field Imaging Mueller polarimetry (IMP) is capable to trace the in-plane orientation of brain fiber tracts by detecting the retardance of healthy brain white matter. IMP can help delineating brain tumor during neurosurgery, because tumor cells grow chaotically. However, the underlying crossing fibers may also affect the retardance of healthy brain. We measured with the transmission Mueller microscope two-layered stacks of thin sections of brain corpus callosum tissue. Brain fiber crossing induced the drop in the linear retardance values and azimuth randomization. The depolarization was invariant to mutual orientation of corpus callosum stripes, hence, the studies of brain tumor depolarization may help to distinguish brain tumor from the fiber crossing zones.
SignificanceMueller matrix (MM) microscopy has proven to be a powerful tool for probing microstructural characteristics of biological samples down to subwavelength scale. However, in clinical practice, doctors usually rely on bright-field microscopy images of stained tissue slides to identify characteristic features of specific diseases and make accurate diagnosis. Cross-modality translation based on polarization imaging helps to improve the efficiency and stability in analyzing sample properties from different modalities for pathologists.AimIn this work, we propose a computational image translation technique based on deep learning to enable bright-field microscopy contrast using snapshot Stokes images of stained pathological tissue slides. Taking Stokes images as input instead of MM images allows the translated bright-field images to be unaffected by variations of light source and samples.ApproachWe adopted CycleGAN as the translation model to avoid requirements on co-registered image pairs in the training. This method can generate images that are equivalent to the bright-field images with different staining styles on the same region.ResultsPathological slices of liver and breast tissues with hematoxylin and eosin staining and lung tissues with two types of immunohistochemistry staining, i.e., thyroid transcription factor-1 and Ki-67, were used to demonstrate the effectiveness of our method. The output results were evaluated by four image quality assessment methods.ConclusionsBy comparing the cross-modality translation performance with MM images, we found that the Stokes images, with the advantages of faster acquisition and independence from light intensity and image registration, can be well translated to bright-field images.
SignificanceImaging Mueller polarimetry is capable to trace in-plane orientation of brain fiber tracts by detecting the optical anisotropy of white matter of healthy brain. Brain tumor cells grow chaotically and destroy this anisotropy. Hence, the drop in scalar retardance values and randomization of the azimuth of the optical axis could serve as the optical marker for brain tumor zone delineation.AimThe presence of underlying crossing fibers can also affect the values of scalar retardance and the azimuth of the optical axis. We studied and analyzed the impact of fiber crossing on the polarimetric images of thin histological sections of brain corpus callosum.ApproachWe used the transmission Mueller microscope for imaging of two-layered stacks of thin sections of corpus callosum tissue to mimic the overlapping brain fiber tracts with different fiber orientations. The decomposition of the measured Mueller matrices was performed with differential and Lu–Chipman algorithms and completed by the statistical analysis of the maps of scalar retardance, azimuth of the optical axis, and depolarization.ResultsOur results indicate the sensitivity of Mueller polarimetry to different spatial arrangement of brain fiber tracts as seen in the maps of scalar retardance and azimuth of optical axis of two-layered stacks of corpus callosum sections The depolarization varies slightly (<15 % ) with the orientation of the optical axes in both corpus callosum stripes, but its value increases by 2.5 to 3 times with the stack thickness.ConclusionsThe crossing brain fiber tracts measured in transmission induce the drop in values of scalar retardance and randomization of the azimuth of the optical axis at optical path length of 15 μm. It suggests that the presence of nerve fibers crossing within the depth of few microns will be also detected in polarimetric maps of brain white matter measured in reflection configuration.
Polarization is capable of probing microstructures and has unique sensitivity to fibrous anisotropic structure. Polarimetric imaging has demonstrated promising potential in diverse applications ranging from biomedicine, material science, and atmospheric remote sensing. The polarization properties of samples can be comprehensively described by a Mueller matrix (MM). However, the relationship between individual MM elements and properties of the sample is often not clear. There have been consistent efforts to derive polarization parameters from MM based on certain assumptions for better description of the samples, e.g., MM polar decomposition (MMPD), MM transformation (MMT) and MM differential decomposition. Usually, the MM imaging requires sequential measurements with different polarization states of incident light and the imaging process is time consuming. In addition, for movable samples, we cannot guarantee the consistency during the imaging. This may cause precision issues since the images cannot be well-registered. In this work, we built a statistical translation model to generate polarization parameters from a single Stokes vector which can be obtained by one-shot imaging. This will improve the imaging efficiency, simplify the optical system and avoid introducing errors by the image registration. In the model design, we adopted the generative adversarial network (GAN) where the generator is based on a U-net architecture. We demonstrated the effectiveness of our approach on liver tissue, blood smear and porous anodic alumina (PAA) film, and quantitatively evaluated the results by similarity assessment methods. The model can generate a parameter image within 0.1 second on a desktop computer, which shows the potential to achieve real-time performance.
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.
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