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 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/axondeepsegComment: 14 pages, 7 figure

Using neurite orientation dispersion and density imaging and tracts constrained by underlying anatomy to differentiate between subjects along the Alzheimer's disease continuum

OBJECTIVE: To assess the involvement of the white matter of the brain in the pathology of Alzheimer’s disease. Using Neurite Orientation Density and Dispersion Imaging (NODDI) and the probabilistic white matter parcellation tool Tracula as a means for understanding whether alterations in the white matter underlie changes in perceived cognitive abilities across the spectrum from health aging to Alzheimer’s disease. METHOD: Data were obtained from 28 participants in the Health Outreach Program for the Elderly (HOPE) at the Boston University Alzheimer’s Disease Center (BU ADC) Clinical Core Registry. MRI scans included an MPRAGE T1 scan, multi-b shell diffusion scan and a High Angular Resolution Diffusion Imaging scan (HARDI). Scans were processed with Freesurfer v6.0 and the NODDI Python2.7 toolkit. The resulting data included the orientation dispersion index (ODI) and Fractional Anisotropy (FA) values for cortical and subcortical regions in the DKT atlas space as well as specific Tracts Constrained by Underlying Anatomy (TRACULA) measurements for 18 specific established white matter tracts. Statistical models using measures of pathway integrity (FA and ODI data) were used to assess relationships with Informant Cognitive Change Index (ICCI), self-described Cognitive Change Index (CCI), and Clinical Dementia Rating (CDR) values. RESULTS: Measures of white matter integrity within several tracts predicted ICCI and CDR well in statistical models. FA and ODI values of the bilateral superior longitudinal fasciculi, inferior longitudinal fasciculi, and the cingulum bundle tracts were all related to ICCI and CDR. None of the known tracts’ FA or ODI values were related to CCI. CONCLUSIONS: Measures of white matter pathway integrity were predictive of ICCI and CDR scores but not CCI. These finding support the notion that self-report of cognitive abilities may be compromised by alterations in insight and reinforce the need for informed study partners and clinical ratings to evaluate potential MCI and AD

Investigation of pathophysiological mechanisms in clinically isolated syndrome using advanced imaging techniques

This thesis concerns an observational study of patients recruited after their first episode of neurological symptoms suggestive of demyelination in the central nervous system and diagnosed either with clinically isolated syndrome or relapsing-remitting multiple sclerosis. In multiple sclerosis, brain tissues can exhibit extensive neuroaxonal microstructural and metabolic abnormalities, but little is known about their presence and significance at the time of the first demyelinating episode. I used a novel multi-parametric quantitative MRI approach, combining neurite orientation dispersion and density imaging (NODDI), which gives information about tissue microstructure, and 23Na MRI, which estimates total sodium concentration, a marker of metabolic dysfunction, in the brains of clinically isolated syndrome patients. I found microstructural and sodium homeostasis alterations in cortical areas of patients that showed clinical relevance. Within the diffuse axonal dispersion found in the normal-appearing white matter, the corpus callosum shared with lesions, signs of axonal damage and metabolic dysfunction, thus emerging as a possible target for early neuroprotective interventions. Structural cortical networks (SCNs) represent patterns of coordinated morphological modifications in cortical areas and they have shown pathophysiological changes in many brain disorders, including multiple sclerosis. I investigated alterations of SCNs at the individual level in this early cohort. Patients showed altered small-world topology, an efficient network organization combining dense local clustering with relatively few long-distance connections. These disruptions were worse for patients with higher lesion load and worse cognitive processing speed indicating that pathophysiological changes in the cortical morphology can influence clinical outcomes. Finally, I hypothesised that the patients in the cohort presenting with optic neuritis may have disturbances in neuropsychological functions related to visual processes. I found that cognitive visuospatial processing is affected after unilateral optic neuritis and improves over time with visual recovery, independently of the structural damage in the visual and central nervous system

Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community

Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring the susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g. in vivo mouse brain data and brains with lesions, which suggests that the network has generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction and high reconstruction speed demonstrate its potential for future applications.Comment: 26 page

Klingler’s method of brain dissection: review of the technique including its usefulness in practical neuroanatomy teaching, neurosurgery and neuroimaging

Klingler’s technique was discovered in the 1930s. It is a modified method of brain fixation and dissection, based on freezing and thawing of the brain tissue, subsequent peeling away of white matter fibres and the gradual exposure of white matter tracts. The added value of this technique is that it is carried out in a stratigraphic manner. This fact makes it an invaluable tool for an in-depth understanding of the complex anatomical organisation of the cerebral hemispheres. The purpose of this paper is to provide a review of Klingler’s method while taking into account the original description of the technique and its value for medical training. The historical background, the concise outline of white matter organisation, as well as our own experience in using this procedure for research and teaching activities were also included. The fibre dissection technique may still be considered an excellent complementary research tool for neuroanatomical studies. Numerous detailed observations about the white matter topography and spatial organisation have been recently made by applying this method. Using this technique may also improve understanding of the three-dimensional intrinsic structure of the brain, which is particularly important both in under- and postgraduate training in the field of neuroanatomy

A connectome of the adult drosophila central brain

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions. Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain

Aberrant visual pathway development in albinism: from retina to cortex

Albinism refers to a group of genetic abnormalities in melanogenesis that are associated neuronal misrouting through the optic chiasm. Previous imaging studies have shown structural alterations at different points along the visual pathway of people with albinism (PWA) including foveal hypoplasia, optic nerve and chiasm size alterations and visual cortex reorganisation, but fail to provide a holistic in-vivo characterisation of the visual neurodevelopmental alterations from retina to visual cortex. We perform quantitative assessment of visual pathway structure and function in 23 PWA and 20 matched controls using optical coherence tomography (OCT), volumetric magnetic resonance imaging (MRI), diffusion tensor imaging and visual evoked potentials (VEP). PWA had a higher streamline decussation index (percentage of total tractography streamlines decussating at the chiasm) compared to controls (Z=-2.24, p=0.025), and streamline decussation index correlated weakly significantly with inter-hemispheric asymmetry measured using VEP (r=0.484, p=0.042). For PWA, a significant correlation was found between foveal development index and total number of streamlines (r=0.662, p less than 0.001). Optic nerve (p=0.001) and tract (p=0.010) width, and chiasm width (P less than 0.001), area (p=0.006) and volume (p=0.005), were significantly smaller in PWA compared to controls. Significant positive correlations were found between peri-papillary retinal nerve fibre layer thickness and optic nerve (r=0.642, p less than 0.001) and tract (r=0.663, p less than 0.001) width. Occipital pole cortical thickness was 6.88% higher (Z=-4.10, p less than 0.001) in PWA and was related to anterior visual pathway structures including foveal retinal pigment epithelium complex thickness (r=-0.579, p=0.005), optic disc (r=0.478, p=0.021) and rim areas (r=0.597, p=0.003). We were unable to demonstrate a significant relationship between OCT-derived foveal or optic nerve measures and MRI-derived chiasm size or streamline decussation index. Non-invasive imaging techniques demonstrate aberrant development throughout the visual pathways of PWA compared to controls. Our novel tractographic demonstration of altered chiasmatic decussation in PWA corresponds to VEP measured cortical asymmetry and is consistent with chiasmatic misrouting in albinism. We also demonstrate a significant relationship between retinal pigment epithelium and visual cortex thickness indicating that retinal pigmentation defects in albinism lead to downstream structural reorganisation of the visual cortex