Automated method for the segmentation and morphometry of nerve fibers in large-scale CARS images of spinal cord tissue

Vibrational spectroscopy and multiphoton microscopy for label-free visualization of nervous system degeneration and regeneration

Neurological disorders, including spinal cord injury, peripheral nerve injury, traumatic brain injury, and neurodegenerative diseases, pose significant challenges in terms of diagnosis, treatment, and understanding the underlying pathophysiological processes. Label-free multiphoton microscopy techniques, such as coherent Raman scattering, two-photon excited autofluorescence, and second and third harmonic generation microscopy, have emerged as powerful tools for visualizing nervous tissue with high resolution and without the need for exogenous labels. Coherent Raman scattering processes as well as third harmonic generation enable label-free visualization of myelin sheaths, while their combination with two-photon excited autofluorescence and second harmonic generation allows for a more comprehensive tissue visualization. They have shown promise in assessing the efficacy of therapeutic interventions and may have future applications in clinical diagnostics. In addition to multiphoton microscopy, vibrational spectroscopy methods such as infrared and Raman spectroscopy offer insights into the molecular signatures of injured nervous tissues and hold potential as diagnostic markers. This review summarizes the application of these label-free optical techniques in preclinical models and illustrates their potential in the diagnosis and treatment of neurological disorders with a special focus on injury, degeneration, and regeneration. Furthermore, it addresses current advancements and challenges for bridging the gap between research findings and their practical applications in a clinical setting.

AimSeg: A machine-learning-aided tool for axon, inner tongue and myelin segmentation

Electron microscopy (EM) images of axons and their ensheathing myelin from both the central and peripheral nervous system are used for assessing myelin formation, degeneration (demyelination) and regeneration (remyelination). The g-ratio is the gold standard measure of assessing myelin thickness and quality, and traditionally is determined from measurements made manually from EM images-a time-consuming endeavour with limited reproducibility. These measurements have also historically neglected the innermost uncompacted myelin sheath, known as the inner tongue. Nonetheless, the inner tongue has been shown to be important for myelin growth and some studies have reported that certain conditions can elicit its enlargement. Ignoring this fact may bias the standard g-ratio analysis, whereas quantifying the uncompacted myelin has the potential to provide novel insights in the myelin field. In this regard, we have developed AimSeg, a bioimage analysis tool for axon, inner tongue and myelin segmentation. Aided by machine learning classifiers trained on transmission EM (TEM) images of tissue undergoing remyelination, AimSeg can be used either as an automated workflow or as a user-assisted segmentation tool. Validation results on TEM data from both healthy and remyelinating samples show good performance in segmenting all three fibre components, with the assisted segmentation showing the potential for further improvement with minimal user intervention. This results in a considerable reduction in time for analysis compared with manual annotation. AimSeg could also be used to build larger, high quality ground truth datasets to train novel deep learning models. Implemented in Fiji, AimSeg can use machine learning classifiers trained in ilastik. This, combined with a user-friendly interface and the ability to quantify uncompacted myelin, makes AimSeg a unique tool to assess myelin growth.

The Need to Shift from Morphological to Structural Assessment for Carotid Plaque Vulnerability

Degree of luminal stenosis is generally considered to be an important indicator for judging the risk of atherosclerosis burden. However, patients with the same or similar degree of stenosis may have significant differences in plaque morphology and biomechanical factors. This study investigated three patients with carotid atherosclerosis within a similar range of stenosis. Using our developed fluid-structure interaction (FSI) modelling method, this study analyzed and compared the morphological and biomechanical parameters of the three patients. Although their degrees of carotid stenosis were similar, the plaque components showed a significant difference. The distribution range of time-averaged wall shear stress (TAWSS) of patient 2 was wider than that of patient 1 and patient 3. Patient 2 also had a much smaller plaque stress compared to the other two patients. There were significant differences in TAWSS and plaque stresses among three patients. This study suggests that plaque vulnerability is not determined by a single morphological factor, but rather by the combined structure. It is necessary to transform the morphological assessment into a structural assessment of the risk of plaque rupture.

Enhanced Automatic Morphometry of Nerve Histological Sections Using Ensemble Learning

There is a need for an automated morphometry algorithm to facilitate the otherwise labor-intensive task of the quantitative histological analysis of neural microscopic images. A benchmark morphometry algorithm is the convolutional neural network Axondeepseg (ADS), which yields a high segmentation accuracy for scanning and transmission electron microscopy images. Nevertheless, it shows decreased accuracy when applied to optical microscopy images, and it has been observed to yield sizable false positives when identifying small-sized neurons within the slides. In this study, ensemble learning is used to enhance the performance of ADS by combining it with the paired image-to-image translation algorithm PairedImageTranslation (PIT). Here, 120 optical microscopy images of peripheral nerves were used to train and test the ensemble learning model and the two base models individually for comparison. The results showed weighted pixel-wise accuracy for the ensemble model of 95.5%, whereas the ADS and PIT yielded accuracies of 93.4% and 90%, respectively. The automated measurements of the axon diameters and myelin thicknesses from the manually marked ground truth images were not statistically different (p = 0.05) from the measurements taken from the same images when segmented using the developed ensemble model, while they were different when they were measured from the segmented images by the two base models individually. The automated measurement of the G ratios indicated a higher similarity to the ground truth testing images for the ensemble model in comparison with the individual base models. The proposed model yielded automated segmentation of the nerve slides, which were sufficiently equivalent to the manual annotations and could be employed for axon diameters and myelin thickness measurements for fully automated histological analysis of the neural images.

High-throughput segmentation of unmyelinated axons by deep learning

Axonal characterizations of connectomes in healthy and disease phenotypes are surprisingly incomplete and biased because unmyelinated axons, the most prevalent type of fibers in the nervous system, have largely been ignored as their quantitative assessment quickly becomes unmanageable as the number of axons increases. Herein, we introduce the first prototype of a high-throughput processing pipeline for automated segmentation of unmyelinated fibers. Our team has used transmission electron microscopy images of vagus and pelvic nerves in rats. All unmyelinated axons in these images are individually annotated and used as labeled data to train and validate a deep instance segmentation network. We investigate the effect of different training strategies on the overall segmentation accuracy of the network. We extensively validate the segmentation algorithm as a stand-alone segmentation tool as well as in an expert-in-the-loop hybrid segmentation setting with preliminary, albeit remarkably encouraging results. Our algorithm achieves an instance-level [Formula: see text] score of between 0.7 and 0.9 on various test images in the stand-alone mode and reduces expert annotation labor by 80% in the hybrid setting. We hope that this new high-throughput segmentation pipeline will enable quick and accurate characterization of unmyelinated fibers at scale and become instrumental in significantly advancing our understanding of connectomes in both the peripheral and the central nervous systems.

MyelTracer: A Semi-Automated Software for Myelin g-Ratio Quantification

In the central and peripheral nervous systems, the myelin sheath promotes neuronal signal transduction. The thickness of the myelin sheath changes during development and in disease conditions like multiple sclerosis. Such changes are routinely detected using electron microscopy through g-ratio quantification. While g-ratio is one of the most critical measurements in myelin studies, a major drawback is that g-ratio quantification is extremely laborious and time-consuming. Here, we report the development and validation of MyelTracer, an installable, stand-alone software for semi-automated g-ratio quantification based on the Open Computer Vision Library (OpenCV). Compared with manual g-ratio quantification, using MyelTracer produces consistent results across multiple tissues and animal ages, as well as in remyelination after optic nerve crush, and reduces total quantification time by 40-60%. With g-ratio measurements via MyelTracer, a known hypomyelination phenotype can be detected in a Williams syndrome mouse model. MyelTracer is easy to use and freely available for Windows and Mac OS X (https://github.com/HarrisonAllen/MyelTracer).

Transport and retention of artificial and real wastewater particles inside a bed of settled aerobic granular sludge assessed applying magnetic resonance imaging

The removal or degradation of particulate organic matter is a crucial part in biological wastewater treatment. This is even more valid with respect to aerobic granular sludge and the impact of particulate organic matter on the formation and stability of the entire granulation process. Before the organic part of the particulate matter can be hydrolyzed and finally degraded by the microorganism, the particles have to be transported towards and retained within the granulated biomass. The understanding of these processes is currently very limited. Thus, the present study aimed at visualizing the transport of particulate organic matter into and through an aerobic granular sludge bed. Magnetic Resonance Imaging (MRI) was successfully applied to resolve the different fractions of a granular sludge bed over time and space. Quantification and merging of 3D data sets allowed for a clear determination of the particle distribution within the granular sludge bed. Dextran coated super paramagnetic iron oxide nanoparticles (SPIONs, d p  =  38 ± 10 nm) served as model particles for colloidal particles. Microcrystalline cellulose particles ( d p  = 1-20 μm) tagged with paramagnetic iron oxide were applied as a reference for toilet paper, which is a major fraction of particulate matter in domestic wastewater. The results were supplemented by the use of real wastewater particles with a size fraction between 28 and 100 μm. Colloidal SPIONs distributed evenly over the granular sludge bed penetrating the granules up to 300 μm. Rinsing the granular sludge bed proved their immobilization. Microcrystalline cellulose and real wastewater particles in the micrometer range accumulated in the void space between settled granules. An almost full retention of the wastewater particles was observed within the first 20 mm of the granular sludge bed. Moreover, the formation of particle layers indicates that most of the micrometer-sized particles are not attached to the biomass and remain mobile. Consequently, these particles are released into the bulk phase when the granulated sludge bed is resuspended.

Construction of a rat spinal cord atlas of axon morphometry

Atlases of the central nervous system are essential for understanding the pathophysiology of neurological diseases, which remains one of the greatest challenges in neuroscience research today. These atlases provide insight into the underlying white matter microstructure and have been created from a variety of animal models, including rats. Although existing atlases of the rat spinal cord provide some details of axon microstructure, there is currently no histological dataset that quantifies axon morphometry exhaustively in the entire spinal cord. In this study, we created the first comprehensive rat spinal cord atlas of the white matter microstructure with quantifiable axon and myelin morphometrics. Using full-slice scanning electron microscopy images and state-of-the-art segmentation algorithms, we generated an atlas of microstructural metrics such as axon diameter, axonal density and g-ratio. After registering the Watson spinal cord white matter atlas to our template, we computed statistics across metrics, spinal levels and tracts. We notably found that g-ratio is relatively constant, whereas axon diameter showed the greatest variation. The atlas, data and full analysis code are freely available at: https://github.com/neuropoly/atlas-rat.

What Has Been Learned from Magnetic Resonance Imaging Examination of the Injured Human Spinal Cord: A Canadian Perspective

Magnetic resonance imaging (MRI) has transformed the way surgeons and researchers study and treat spinal cord injury. In this narrative review, we explore the historical context of imaging the human spinal cord and describe how MRI has evolved from providing the first visualization of the human spinal cord in the 1980s to a remarkable set of imaging tools today. The article focuses in particular on the role of Canadian researchers to this field. We begin by outlining the clinical context of traumatic injury to the human spinal cord and describe why current MRI standards fall short when it comes to treating this disabling condition. Parts 2 and 3 of this work explore an exciting and dramatic shift in the use of MRI technology to aid in our understanding and treatment of traumatic injury to the spinal cord. We explore the use of functional imaging (part 2) and structural imaging (part 3) and explore how these techniques have evolved, how they are used, and the challenges that we face for continued refinement and application to patients who live with the neurological and functional deficits caused by injury to the delicate spinal cord.

Measurement-oriented deep-learning workflow for improved segmentation of myelin and axons in high-resolution images of human cerebral white matter

BACKGROUND

Standard segmentation of high-contrast electron micrographs (EM) identifies myelin accurately but does not translate easily into measurements of individual axons and their myelin, even in cross-sections of parallel fibers. We describe automated segmentation and measurement of each myelinated axon and its sheath in EMs of arbitrarily oriented human white matter from autopsies.

NEW METHODS

Preliminary segmentation of myelin, axons and background by machine learning, using selected filters, precedes automated correction of systematic errors. Final segmentation is done by a deep neural network (DNN). Automated measurement of each putative fiber rejects measures encountering pre-defined artifacts and excludes fibers failing to satisfy pre-defined conditions.

RESULTS

Improved segmentation of three sets of 30 annotated images each (two sets from human prefrontal white matter and one from human optic nerve) is achieved with a DNN trained only with a subset of the first set from prefrontal white matter. Total number of myelinated axons identified by the DNN differed from expert segmentation by 0.2%, 2.9%, and -5.1%, respectively. G-ratios differed by 2.96%, 0.74% and 2.83%. Intraclass correlation coefficients between DNN and annotated segmentation were mostly >0.9, indicating nearly interchangeable performance.

COMPARISON WITH EXISTING METHOD(S)

Measurement-oriented studies of arbitrarily oriented fibers from central white matter are rare. Published methods are typically applied to cross-sections of fascicles and measure aggregated areas of myelin sheaths and axons, allowing estimation only of average g-ratio.

CONCLUSIONS

Automated segmentation and measurement of axons and myelin is complex. We report a feasible approach that has so far proven comparable to manual segmentation.

Axons morphometry in the human spinal cord

Due to the technical challenges of large-scale microscopy and analysis, to date only limited knowledge has been made available about axon morphometry (diameter, shape, myelin thickness, volume fraction), thereby limiting our understanding of neuronal microstructure and slowing down research on neurodegenerative pathologies. This study addresses this knowledge gap by establishing a state-of-the-art acquisition and analysis framework for mapping axon morphometry, and providing the first comprehensive mapping of axon morphometry in the human spinal cord. We dissected, fixed and stained a human spinal cord with osmium tetroxide, and used a scanning electron microscope to image the entirety of 23 axial slices, covering C1 to L5 spinal levels. An automatic method based on deep learning was then used to segment each axon and myelin sheath to produce maps of axon morphometry. These maps were then registered to a standard spinal cord magnetic resonance imaging (MRI) template. Between 500,000 (lumbar) and 1 million (cervical) myelinated axons were segmented at each level of this human spinal cord. Morphometric features show a large disparity between tracts, but high right-left symmetry. Our results suggest a modality-based organization of the dorsal column in the human, as it has been observed in the rat. The generated axon morphometry template is publicly available at https://osf.io/8k7jr/ and could be used as a reference for quantitative MRI studies. The proposed framework for axon morphometry mapping could be extended to other parts of the central or peripheral nervous system that exhibit coherently-oriented axons.

Microstructural imaging in the spinal cord and validation strategies

In vivo histology using magnetic resonance imaging (MRI) is a newly emerging research field that aims to non-invasively characterize tissue microstructure. The implications of in vivo histology are many, from discovering novel biomarkers to studying human development, to providing tools for disease diagnosis and monitoring the effects of novel treatments on tissue. This review focuses on quantitative MRI (qMRI) techniques that are used to map spinal cord microstructure. Opening with a rationale for non-invasive imaging of the spinal cord, this article continues with a brief overview of the existing MRI techniques for axon and myelin imaging, followed by the specific challenges and potential solutions for acquiring and processing such data. The final part of this review focuses on histological validation, with suggested tissue preparation, acquisition and processing protocols for large-scale microscopy.

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphometry across species, or to validate novel non-invasive quantitative magnetic resonance imaging techniques. Most currently-available segmentation algorithms are based on standard image processing and usually require multiple processing steps and/or parameter tuning by the user to adapt to different modalities. Moreover, only a few methods are publicly available. We introduce AxonDeepSeg, an open-source software that performs axon and myelin segmentation of microscopic images using deep learning. AxonDeepSeg features: (i) a convolutional neural network architecture; (ii) an easy training procedure to generate new models based on manually-labelled data and (iii) two ready-to-use models trained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results show high pixel-wise accuracy across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and 84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed and morphological metrics are extracted and compared against the literature. AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepseg .

Axon and Myelin Morphology in Animal and Human Spinal Cord

Characterizing precisely the microstructure of axons, their density, size and myelination is of interest for the neuroscientific community, for example to help maximize the outcome of studies on white matter (WM) pathologies of the spinal cord (SC). The existence of a comprehensive and structured database of axonal measurements in healthy and disease models could help the validation of results obtained by different researchers. The purpose of this article is to provide such a database of healthy SC WM, to discuss the potential sources of variability and to suggest avenues for robust and accurate quantification of axon morphometry based on novel acquisition and processing techniques. The article is organized in three sections. The first section reviews morphometric results across species according to range of densities and counts of myelinated axons, axon diameter and myelin thickness, and characteristics of unmyelinated axons in different regions. The second section discusses the sources of variability across studies, such as age, sex, spinal pathways, spinal levels, statistical power and terminology in regard to tracts and protocols. The third section presents new techniques and perspectives that could benefit histology studies. For example, coherent anti-stokes Raman spectroscopy (CARS) imaging can provide sub-micrometric resolution without the need for fixation and staining, while slide scanners and stitching algorithms can provide full cross-sectional area of SC. In combination with these acquisition techniques, automatic segmentation algorithms for delineating axons and myelin sheath can help provide large-scale statistics on axon morphometry.

Lipid Order Degradation in Autoimmune Demyelination Probed by Polarized Coherent Raman Microscopy

Myelin around axons is currently widely studied by structural analyses and large-scale imaging techniques, with the goal to decipher its critical role in neuronal protection. Although there is strong evidence that in myelin, lipid composition, and lipid membrane morphology are affected during the progression of neurodegenerative diseases, there is no quantitative method yet to report its ultrastructure in tissues at both molecular and macroscopic levels, in conditions potentially compatible with in vivo observations. In this work, we study and quantify the molecular order of lipids in myelin at subdiffraction scales, using label-free polarization-resolved coherent anti-Stokes Raman, which exploits coherent anti-Stokes Raman sensitivity to coupling between light polarization and oriented molecular vibrational bonds. Importantly, the method does not use any a priori parameters in the sample such as lipid type, orientational organization, and composition. We show that lipid molecular order of myelin in the mouse spinal cord is significantly reduced throughout the progression of experimental autoimmune encephalomyelitis, a model for multiple sclerosis, even in myelin regions that appear morphologically unaffected. This technique permits us to unravel molecular-scale perturbations of lipid layers at an early stage of the demyelination progression, whereas the membrane architecture at the mesoscopic scale (here ∼100 nm) seems much less affected. Such information cannot be brought by pure morphological observation and, to our knowledge, brings a new perspective to molecular-scale understanding of neurodegenerative diseases.

AxonPacking: An Open-Source Software to Simulate Arrangements of Axons in White Matter

HIGHLIGHTS

AxonPacking: Open-source software for simulating white matter microstructure.

Validation on a theoretical disk packing problem.

Reproducible and stable for various densities and diameter distributions.

Can be used to study interplay between myelin/fiber density and restricted fraction.

Quantitative Magnetic Resonance Imaging (MRI) can provide parameters that describe white matter microstructure, such as the fiber volume fraction (FVF), the myelin volume fraction (MVF) or the axon volume fraction (AVF) via the fraction of restricted water (fr). While already being used for clinical application, the complex interplay between these parameters requires thorough validation via simulations. These simulations required a realistic, controlled and adaptable model of the white matter axons with the surrounding myelin sheath. While there already exist useful algorithms to perform this task, none of them combine optimisation of axon packing, presence of myelin sheath and availability as free and open source software. Here, we introduce a novel disk packing algorithm that addresses these issues. The performance of the algorithm is tested in term of reproducibility over 50 runs, resulting density, and stability over iterations. This tool was then used to derive multiple values of FVF and to study the impact of this parameter on fr and MVF in light of the known microstructure based on histology sample. The standard deviation of the axon density over runs was lower than 10⁻³ and the expected hexagonal packing for monodisperse disks was obtained with a density close to the optimal density (obtained: 0.892, theoretical: 0.907). Using an FVF ranging within [0.58, 0.82] and a mean inter-axon gap ranging within [0.1, 1.1] μm, MVF ranged within [0.32, 0.44] and fr ranged within [0.39, 0.71], which is consistent with the histology. The proposed algorithm is implemented in the open-source software AxonPacking (https://github.com/neuropoly/axonpacking) and can be useful for validating diffusion models as well as for enabling researchers to study the interplay between microstructure parameters when evaluating qMRI methods.

Advances in Intravital Non-Linear Optical Imaging of the Central Nervous System in Rodents

Purpose of review: Highly coordinated cellular interactions occur in the healthy or pathologic adult rodent central nervous system (CNS). Until recently, technical challenges have restricted the analysis of these events to largely static modes of study such as immuno-fluorescence and electron microscopy on fixed tissues. The development of intravital imaging with subcellular resolution is required to probe the dynamics of these events in their natural context, the living brain. Recent findings: This review focuses on the recently developed live non-linear optical imaging modalities, the core principles involved, the identified technical challenges that limit their use and the scope of their applications. We highlight some practical applications for these modalities with a specific attention given to Experimental Autoimmune Encephalomyelitis (EAE), a rodent model of a chronic inflammatory disease of the CNS characterized by the formation of disseminated demyelinating lesions accompanied by axonal degeneration. Summary: We conclude that label-free nonlinear optical imaging combined to two photon imaging will continue to contribute richly to comprehend brain function and pathogenesis and to develop effective therapeutic strategies.

Probing pain pathways with light

We have witnessed an accelerated growth of photonics technologies in recent years to enable not only monitoring the activity of specific neurons, while animals are performing certain types of behavior, but also testing whether specific cells, circuits, and regions are sufficient or necessary for initiating, maintaining, or altering this or that behavior. Compared to other sensory systems, however, such as the visual or olfactory system, photonics applications in pain research are only beginning to emerge. One reason pain studies have lagged behind is that many of the techniques originally developed cannot be directly implemented to study key relay sites within pain pathways, such as the skin, dorsal root ganglia, spinal cord, and brainstem. This is due, in part, to difficulties in accessing these structures with light. Here we review a number of recent advances in design and delivery of light-sensitive molecular probes (sensors and actuators) into pain relay circuits to help decipher their structural and functional organization. We then discuss several challenges that have hampered hardware access to specific structures including light scattering, tissue movement and geometries. We review a number of strategies to circumvent these challenges, by delivering light into, and collecting it from the different key sites to unravel how nociceptive signals are encoded at each level of the neuraxis. We conclude with an outlook on novel imaging modalities for label-free chemical detection and opportunities for multimodal interrogation in vivo. While many challenges remain, these advances offer unprecedented opportunities to bridge cellular approaches with context-relevant behavioral testing, an essential step toward improving translation of basic research findings into clinical applications.

Intravital assessment of myelin molecular order with polarimetric multiphoton microscopy

Myelin plays an essential role in the nervous system and its disruption in diseases such as multiple sclerosis may lead to neuronal death, thus causing irreversible functional impairments. Understanding myelin biology is therefore of fundamental and clinical importance, but no tools currently exist to describe the fine spatial organization of myelin sheaths in vivo. Here we demonstrate intravital quantification of the myelin molecular structure using a microscopy method based on polarization-resolved coherent Raman scattering. Developmental myelination was imaged noninvasively in live zebrafish. Longitudinal imaging of individual axons revealed changes in myelin organization beyond the diffraction limit. Applied to promyelination drug screening, the method uniquely enabled the identification of focal myelin regions with differential architectures. These observations indicate that the study of myelin biology and the identification of therapeutic compounds will largely benefit from a method to quantify the myelin molecular organization in vivo.

AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis

Segmenting axon and myelin from microscopic images is relevant for studying the peripheral and central nervous system and for validating new MRI techniques that aim at quantifying tissue microstructure. While several software packages have been proposed, their interface is sometimes limited and/or they are designed to work with a specific modality (e.g., scanning electron microscopy (SEM) only). Here we introduce AxonSeg, which allows to perform automatic axon and myelin segmentation on histology images, and to extract relevant morphometric information, such as axon diameter distribution, axon density and the myelin g-ratio. AxonSeg includes a simple and intuitive MATLAB-based graphical user interface (GUI) and can easily be adapted to a variety of imaging modalities. The main steps of AxonSeg consist of: (i) image pre-processing; (ii) pre-segmentation of axons over a cropped image and discriminant analysis (DA) to select the best parameters based on axon shape and intensity information; (iii) automatic axon and myelin segmentation over the full image; and (iv) atlas-based statistics to extract morphometric information. Segmentation results from standard optical microscopy (OM), SEM and coherent anti-Stokes Raman scattering (CARS) microscopy are presented, along with validation against manual segmentations. Being fully-automatic after a quick manual intervention on a cropped image, we believe AxonSeg will be useful to researchers interested in large throughput histology. AxonSeg is open source and freely available at: https://github.com/neuropoly/axonseg.

Beyond the borders--Biomedical applications of non-linear Raman microscopy

Raman spectroscopy offers great promise for label free imaging in biomedical applications. Its use, however, is hampered by the long integration times required and the presence of autofluorescence in many samples which outshines the Raman signals. In order to overcome these limitations, a variety of different non-linear Raman imaging techniques have been developed over the last decade. This review describes biomedical applications of these novel but already mature imaging techniques.

Quantitative analysis of the myelin g-ratio from electron microscopy images of the macaque corpus callosum

We provide a detailed morphometric analysis of eight transmission electron micrographs (TEMs) obtained from the corpus callosum of one cynomolgus macaque. The raw TEM images are included in the article, along with the distributions of the axon caliber and the myelin g-ratio in each image. The distributions are analyzed to determine the relationship between axon caliber and g-ratio, and compared against the aggregate metrics (myelin volume fraction, fiber volume fraction, and the aggregate g-ratio), as defined in the accompanying research article entitled 'In vivo histology of the myelin g-ratio with magnetic resonance imaging' (Stikov et al., NeuroImage, 2015).

Quantitative analysis of light scattering in polarization-resolved nonlinear microscopy

Polarization resolved nonlinear microscopy (PRNM) is a powerful technique to gain microscopic structural information in biological media. However, deep imaging in a variety of biological specimens is hindered by light scattering phenomena, which not only degrades the image quality but also affects the polarization state purity. In order to quantify this phenomenon and give a framework for polarization resolved microscopy in thick scattering tissues, we develop a characterization methodology based on four wave mixing (FWM) process. More specifically, we take advantage of two unique features of FWM, meaning its ability to produce an intrinsic in-depth local coherent source and its capacity to quantify the presence of light depolarization in isotropic regions inside a sample. By exploring diverse experimental layouts in phantoms with different scattering properties, we study systematically the influence of scattering on the nonlinear excitation and emission processes. The results show that depolarization mechanisms for the nonlinearly generated photons are highly dependent on the scattering center size, the geometry used (epi/forward) and, most importantly, on the thickness of the sample. We show that the use of an un-analyzed detection makes the polarization-dependence read-out highly robust to scattering effects, even in regimes where imaging might be degraded. The effects are illustrated in polarization resolved imaging of myelin lipid organization in mouse spinal cords.