Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images

From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

Neural crest origin of sympathetic neurons at the dawn of vertebrates

The neural crest is an embryonic stem cell population unique to vertebrates1 whose expansion and diversification are thought to have promoted vertebrate evolution by enabling emergence of new cell types and structures such as jaws and peripheral ganglia2. Although jawless vertebrates have sensory ganglia, convention has it that trunk sympathetic chain ganglia arose only in jawed vertebrates3-8. Here, by contrast, we report the presence of trunk sympathetic neurons in the sea lamprey, Petromyzon marinus, an extant jawless vertebrate. These neurons arise from sympathoblasts near the dorsal aorta that undergo noradrenergic specification through a transcriptional program homologous to that described in gnathostomes. Lamprey sympathoblasts populate the extracardiac space and extend along the length of the trunk in bilateral streams, expressing the catecholamine biosynthetic pathway enzymes tyrosine hydroxylase and dopamine β-hydroxylase. CM-DiI lineage tracing analysis further confirmed that these cells derive from the trunk neural crest. RNA sequencing of isolated ammocoete trunk sympathoblasts revealed gene profiles characteristic of sympathetic neuron function. Our findings challenge the prevailing dogma that posits that sympathetic ganglia are a gnathostome innovation, instead suggesting that a late-developing rudimentary sympathetic nervous system may have been characteristic of the earliest vertebrates.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review

Detection of curvilinear structures from microscopic images, which help the clinicians to make an unambiguous diagnosis is assuming paramount importance in recent clinical practice. Appearance and size of dermatophytic hyphae, keratitic fungi, corneal and retinal vessels vary widely making their automated detection cumbersome. Automated deep learning methods, endowed with superior self-learning capacity, have superseded the traditional machine learning methods, especially in complex images with challenging background. Automatic feature learning ability using large input data with better generalization and recognition capability, but devoid of human interference and excessive pre-processing, is highly beneficial in the above context. Varied attempts have been made by researchers to overcome challenges such as thin vessels, bifurcations and obstructive lesions in retinal vessel detection as revealed through several publications reviewed here. Revelations of diabetic neuropathic complications such as tortuosity, changes in the density and angles of the corneal fibers have been successfully sorted in many publications reviewed here. Since artifacts complicate the images and affect the quality of analysis, methods addressing these challenges have been described. Traditional and deep learning methods, that have been adapted and published between 2015 and 2021 covering retinal vessels, corneal nerves and filamentous fungi have been summarized in this review. We find several novel and meritorious ideas and techniques being put to use in the case of retinal vessel segmentation and classification, which by way of cross-domain adaptation can be utilized in the case of corneal and filamentous fungi also, making suitable adaptations to the challenges to be addressed.

Centella asiatica (L.) Urban. Attenuates Cell Damage in Hydrogen Peroxide-Induced Oxidative Stress in Transgenic Murine Embryonic Stem Cell Line-Derived Neural-Like Cells: A Preliminary Study for Potential Treatment of Alzheimer's Disease

BACKGROUND

Centella asiatica (L.) (C. asiatica) is commonly known in South East and South East Asia communities for its nutritional and medicinal benefits. Besides being traditionally used to enhance memory and accelerate wound healing, its phytochemicals have been extensively documented for their neuroprotective, neuroregenerative, and antioxidant properties.

OBJECTIVE

The present study aims to investigate the effects of a standardized raw extract of C. asiatica (RECA) on hydrogen peroxide (H2O2)-induced oxidative stress and apoptotic death in neural-like cells derived from mouse embryonic stem (ES) cell line.

METHODS

A transgenic mouse ES cell (46C) was differentiated into neural-like cells using 4-/4+ protocol with addition of all-trans retinoic acid. These cells were then exposed to H2O2 for 24 h. The effects of RECA on H2O2-induced neural-like cells were assessed through cell viability, apoptosis, and reactive oxygen species (ROS) assays, as well as neurite length measurement. The gene expression levels of neuronal-specific and antioxidant markers were assessed by RT-qPCR analysis.

RESULTS

Pre-treatment with H2O2 for 24 hours, in a dose-dependent manner, damaged neural-like cells as marked by a decrease in cell viability, substantial increase in intracellular ROS accumulation, and increase in apoptotic rate compared to untreated cells. These cells were used to treat with RECA. Treatment with RECA for 48 h remarkably restored cell survival and promoted neurite outgrowth in the H2O2- damaged neurons by increasing cell viability and decreasing ROS activity. RT-qPCR analysis revealed that RECA upregulated the level of antioxidant genes such as thioredoxin-1 (Trx-1) and heme oxygenase-1 (HO-1) of treated cells, as well as the expression level of neuronal-specific markers such as Tuj1 and MAP2 genes, suggesting their contribution in neuritogenic effect.

CONCLUSION

Our findings indicate that RECA promotes neuroregenerative effects and exhibits antioxidant properties, suggesting a valuable synergistic activity of its phytochemical constituents, thus, making the extract a promising candidate in preventing or treating oxidative stress-associated Alzheimer's disease.

Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test

Neuron morphology gives rise to distinct axons and dendrites and plays an essential role in neuronal functionality and circuit dynamics. In rat hippocampal neurons, morphological development occurs over roughly one week in vitro. This development has been qualitatively described as occurring in 5 stages. Still, there is a need to quantify cell growth to monitor cell culture health, understand cell responses to sensory cues, and compare experimental results and computational growth model predictions. To address this need, embryonic rat hippocampal neurons were observed in vitro over six days, and their processes were quantified using both standard morphometrics (degree, number of neurites, total length, and tortuosity) and new metrics (distance between change points, relative turning angle, and the number of change points) based on the Change-Point Test to track changes in path trajectories. Of the standard morphometrics, the total length of neurites per cell and the number of endpoints were significantly different between 0.5, 1.5, and 4 days in vitro, which are typically associated with Stages 2-4. Using the Change-Point Test, the number of change points and the average distance between change points per cell were also significantly different between those key time points. This work highlights key quantitative characteristics, both among common and novel morphometrics, that can describe neuron development in vitro and provides a foundation for analyzing directional changes in neurite growth for future studies.

Treadmill Exercise Prevents Decline in Spatial Learning and Memory in 3×Tg-AD Mice through Enhancement of Structural Synaptic Plasticity of the Hippocampus and Prefrontal Cortex

Alzheimer’s disease (AD) is characterized by deficits in learning and memory. A pathological feature of AD is the alterations in the number and size of synapses, axon length, dendritic complexity, and dendritic spine numbers in the hippocampus and prefrontal cortex. Treadmill exercise can enhance synaptic plasticity in mouse or rat models of stroke, ischemia, and dementia. The aim of this study was to examine the effects of treadmill exercise on learning and memory, and structural synaptic plasticity in 3×Tg-AD mice, a mouse model of AD. Here, we show that 12 weeks treadmill exercise beginning in three-month-old mice improves spatial working memory in six-month-old 3×Tg-AD mice, while non-exercise six-month-old 3×Tg-AD mice exhibited impaired spatial working memory. To investigate potential mechanisms for the treadmill exercise-induced improvement of spatial learning and memory, we examined structural synaptic plasticity in the hippocampus and prefrontal cortex of six-month-old 3×Tg-AD mice that had undergone 12 weeks of treadmill exercise. We found that treadmill exercise led to increases in synapse numbers, synaptic structural parameters, the expression of synaptophysin (Syn, a presynaptic marker), the axon length, dendritic complexity, and the number of dendritic spines in 3×Tg-AD mice and restored these parameters to similar levels of non-Tg control mice without treadmill exercise. In addition, treadmill exercise also improved these parameters in non-Tg control mice. Strengthening structural synaptic plasticity may represent a potential mechanism by which treadmill exercise prevents decline in spatial learning and memory and synapse loss in 3×Tg-AD mice.

Neuron tracing and quantitative analyses of dendritic architecture reveal symmetrical three-way-junctions and phenotypes of git-1 in C. elegans

Complex dendritic trees are a distinctive feature of neurons. Alterations to dendritic morphology are associated with developmental, behavioral and neurodegenerative changes. The highly-arborized PVD neuron of C. elegans serves as a model to study dendritic patterning; however, quantitative, objective and automated analyses of PVD morphology are missing. Here, we present a method for neuronal feature extraction, based on deep-learning and fitting algorithms. The extracted neuronal architecture is represented by a database of structural elements for abstracted analysis. We obtain excellent automatic tracing of PVD trees and uncover that dendritic junctions are unevenly distributed. Surprisingly, these junctions are three-way-symmetrical on average, while dendritic processes are arranged orthogonally. We quantify the effect of mutation in git-1, a regulator of dendritic spine formation, on PVD morphology and discover a localized reduction in junctions. Our findings shed new light on PVD architecture, demonstrating the effectiveness of our objective analyses of dendritic morphology and suggest molecular control mechanisms.

Nerve cells (neurons) collect input signals via branched cellular projections called dendrites. A major aspect of the study of neurons, dating back over a century, involves the characterization of neuronal shapes and of their dendritic processes.

Here, we present an algorithmic approach for detection and classification of the tree-like dendrites of the PVD neuron in C. elegans worms. A key feature of our approach is to represent dendritic trees by a set of fundamental shapes, such as junctions and linear elements. By analyzing this dataset, we discovered several novel structural features. We have found that the junctions connecting branched dendrites have a three-way-symmetry, although the dendrites are arranged in a crosshatch pattern, and that the distribution of junctions varies across distinct sub-classes of the PVD’s dendritic tree. We further quantified subtle morphological effects due to mutation in the git-1 gene, a known regulator of dendritic spines. Our findings suggest molecular mechanisms for dendritic shape regulation and may help direct new avenues of research.

A CAR RNA FISH assay to study functional and spatial heterogeneity of chimeric antigen receptor T cells in tissue

Chimeric antigen receptor (CAR) T cells are engineered cells used in cancer therapy and are studied to treat infectious diseases. Trafficking and persistence of CAR T cells is an important requirement for efficacy to target cancer. Here, we describe a CAR RNA FISH histo-cytometry platform combined with a random reaction seed image analysis algorithm to quantitate spatial distribution and in vivo functional activity of a CAR T cell population at a single cell resolution for preclinical models. In situ, CAR T cell exhibited a heterogenous effector gene expression and this was related to the distance from tumor cells, allowing a quantitative assessment of the potential in vivo effectiveness. The platform offers the potential to study immune functions of genetically engineered cells in situ with their target cells in tissues with high statistical power and thus, can serve as an important tool for preclinical assessment of CAR T cell effectiveness.

Cytotoxic and biomechanical effects of clinical dosing schemes of paclitaxel on neurons and cancer cells

PURPOSE

Chemotherapy-induced peripheral neuropathy often results in a reduction in drug dose. However, the serum level of anticancer drugs varies with time after intravenous infusion, and this factor has seldom been considered in previous in vitro studies. The goals of this study were to build an automatic dosage control system and to evaluate the influence of drug infusion rate on the cells.

METHODS

Neurons and melanoma cells were used as the samples. The 3-h (average and peak concentration: 0.024 and 0.287 μM) and 24-h infusion (average and peak concentration: 0.020 and 0.042 μM) schemes were investigated. For evaluations, cell indentation tests by an atomic force microscope, serial immunofluorescent images, and cell viability analysis was performed.

RESULTS

For the neurons, Young's modulus first increased and then remained unchanged in the 3-h scheme, but was stationary throughout the observation period in the 24-h scheme. For the cancer cells, Young's modulus increased in both infusion schemes, and the increase was larger in the 3-h scheme. Morphologically, axons swelled and shortened, and the number of their branches decreased in the 3-h scheme. In contrast, there was only slowed growth of axons without obvious morphological changes in the 24-h scheme. Viability analysis of the cancer cells revealed that the 3-h scheme had a better anticancer effect.

CONCLUSION

A dosage-control system simulating the pharmacodynamic changes of drugs was successfully constructed for in vitro cell cultures. The 3-h scheme of paclitaxel showed better anticancer effects but more adverse effects on neuronal growth and morphology.

The natverse, a versatile toolbox for combining and analysing neuroanatomical data

To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the natverse. The natverse allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the natverse enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The natverse also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The natverse is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community.

A new robust method for blood vessel segmentation in retinal fundus images based on weighted line detector and hidden Markov model

BACKGROUND AND OBJECTIVE

Automatic vessel segmentation is a crucial preliminary processing step to facilitate ophthalmologist diagnosis in some diseases. But, due to the complexity of retinal fundus image, there are some problems on accurate segmentation of retinal vessel. In this paper, a new method for retinal vessel segmentation is proposed to handle two main problems: thin vessel missing and false detection in difficult regions.

METHODS

First, an improved line detector is proposed and used to fast extract the major structures of vessels. Then, Hidden Markov model (HMM) is applied to effectively detect vessel centerlines that include thin vessels. Finally, a denoising approach is presented to remove noises and two types of vessels are unified to obtain the complete segmentation results.

RESULTS

Our method is tested on two public databases (DRIVE and STARE databases), and five measures namely accuracy (Acc), sensitivity (Se), specificity (Sp), Dice coefficient (Dc), structural similarity index (SSIM) and feature similarity index (FSIM) are used to evaluate our segmentation performance. The respective values of the performance measures are 0.9475, 0.7262, 0.9803, 0.7781, 0.9992 and 0.9793 for DRIVE dataset and 0.9535, 0.7865, 0.9730, 0.7764, 0.9987 and 0.9742 for STARE dataset.

CONCLUSIONS

The experiment results show that our method outperforms most published state-of-the-art methods and is better the result of a human observer. Moreover, in term of specificity, our proposed algorithm can obtain the best score among the unsupervised methods. Meanwhile, there are excellent structure and feature similarities between our result and the ground truth according to achieved SSIM and FSIM. Visual inspection on the segmentation results shows that the proposed method produces more accurate segmentations on some difficult regions such as optic disc and central light reflex while detecting thin vessels effectively compared with the other methods.

Fungal feature tracker (FFT): A tool for quantitatively characterizing the morphology and growth of filamentous fungi

Filamentous fungi are ubiquitous in nature and serve as important biological models in various scientific fields including genetics, cell biology, ecology, evolution, and chemistry. A significant obstacle in studying filamentous fungi is the lack of tools for characterizing their growth and morphology in an efficient and quantitative manner. Consequently, assessments of the growth of filamentous fungi are often subjective and imprecise. In order to remedy this problem, we developed Fungal Feature Tracker (FFT), a user-friendly software comprised of different image analysis tools to automatically quantify different fungal characteristics, such as spore number, spore morphology, and measurements of total length, number of hyphal tips and the area covered by the mycelium. In addition, FFT can recognize and quantify specialized structures such as the traps generated by nematode-trapping fungi, which could be tuned to quantify other distinctive fungal structures in different fungi. We present a detailed characterization and comparison of a few fungal species as a case study to demonstrate the capabilities and potential of our software. Using FFT, we were able to quantify various features at strain and species level, such as mycelial growth over time and the length and width of spores, which would be difficult to track using classical approaches. In summary, FFT is a powerful tool that enables quantitative measurements of fungal features and growth, allowing objective and precise characterization of fungal phenotypes.

One of the main obstacles to study filamentous fungi is the lack of tools for characterizing fungal phenotypes in an efficient and quantitative manner. Assessment of cell growth and numbers rely on tedious manual techniques that often result in subjective and imprecise measurements. In response to those limitations, we developed Fungal Feature Tracker (FFT), a user-friendly software that allows researchers to characterize different phenotypic features of filamentous fungi such as sporulation, spore morphology and mycelial growth. In addition, FFT can recognize and quantify other fungal structures including the fungal traps developed by nematode-trapping fungi. In order to show the capabilities and potential of our software, we conducted a detailed characterization and comparison of different fungal species. Our comparison relies on a series of experimental set-ups using standard and easily accessible equipment to ensure reproducibility in other laboratories. In summary, FFT is an easy to use and powerful tool that can quantitatively characterize fungal morphology, cell number and quantitatively measures the filamentous growth, which will advance our understanding of the growth and biology of filamentous fungi.

Quantification of surviving neurons after contusion, dislocation, and distraction spinal cord injuries using automated methods

This study proposes and validates an automated method for counting neurons in spinal cord injury (SCI) and then uses it to examine and compare the surviving cells in common types of SCI mechanisms. Moderate contusion, dislocation, and distraction SCIs were surgically induced in Sprague Dawley male rats (n = 6 for each type of injury). Their spinal cords were harvested 8 weeks post injury with 5 normal weight-matched rats. The spinal cords were cut, stained with anti-NeuN antibody and fluorescent Nissl, and imaged in the dorsal and ventral horns at various distances to the epicenter. Neurons in the images were automatically counted using an algorithm that was designed to filter non-soma-like objects based on morphological characteristics (size, solidity, circular pattern) and check the remaining objects for the double-stained nucleus/cell body features (brightness variation, brightness distribution, color). To validate the automated method, some of the images were randomly selected for manual counting. The number of surviving cells that were automatically measured by the algorithm was found to be correlated with the values that were manually measured by 2 observers (P < .001) with similar differences (P > .05). Neurons in the dorsal and ventral horns were reduced after the SCIs (P < .05). Dislocation and distraction, respectively, caused the most severe damage to the ventral horn neurons especially near the epicenter and the most extensive and uniform damage to the dorsal horn neurons (P < .05). Our method was proved to be reliable, which is suitable for studying different types of SCI.

Random-Reaction-Seed Method for Automated Identification of Neurite Elongation and Branching

Conventional deterministic algorithms (i.e., skeletonization and edge-detection) lack robustness and sensitivity to reliably detect the neurite elongation and branching of low signal-to-noise-ratio microscopy images. Neurite outgrowth experiments produce an enormous number of images that require automated measurement; however, the tracking of neurites is easily lost in the automated process due to the intrinsic variability of neurites (either axon or dendrite) under stimuli. We have developed a stochastic random-reaction-seed (RRS) method to identify neurite elongation and branching accurately and automatically. The random-seeding algorithm of RRS is based on the hidden-Markov-model (HMM) to offer a robust enough way for tracing arbitrary neurite structures, while the reaction-seeding algorithm of RRS secures the efficiency of random seeding. It is noteworthy that RRS is capable of tracing a whole neurite branch by only one initial seed, so that RRS is proficient at quantifying extensive amounts of neurite outgrowth images with noisy background in microfluidic devices of biomedical engineering fields. The method also showed notable performance for reconstructing of net-like structures, and thus is expected to be proficient for biomedical feature extractions in a wide range of applications, such as retinal vessel segmentation and cell membrane profiling in spurious-edge-tissues.

Soma Detection in 3D Images of Neurons using Machine Learning Technique

Computing and analyzing the neuronal structure is essential to studying connectome. Two important tasks for such analysis are finding the soma and constructing the neuronal structure. Finding the soma is considered more important because it is required for some neuron tracing algorithms. We describe a robust automatic soma detection method developed based on the machine learning technique. Images of neurons were three-dimensional confocal microscopic images in the FlyCircuit database. The testing data were randomly selected raw images that contained noises and partial neuronal structures. The number of somas in the images was not known in advance. Our method tries to identify all the somas in the images. Experimental results showed that the method is efficient and robust.

DEFiNE: A Method for Enhancement and Quantification of Fluorescently Labeled Axons

Visualization and quantification of fluorescently labeled axonal fibers are widely employed in studies of neuronal connectivity in the brain. However, accurate analysis of axon density is often confounded by autofluorescence and other fluorescent artifacts. By the time these problems are detected in labeled tissue sections, significant time and resources have been invested, and the tissue may not be easy to replace. In response to these difficulties, we have developed Digital Enhancement of Fibers with Noise Elimination (DEFiNE), a method for eliminating fluorescent artifacts from digital images based on their morphology and fluorescence spectrum, thus permitting enhanced visualization and quantification of axonal fibers. Application of this method is facilitated by a DEFiNE macro, written using ImageJ Macro Language (IJM), which includes an automated and customizable procedure for image processing and a semi-automated quantification method that accounts for any remaining local variation in background intensity. The DEFiNE macro is open-source and used with the widely available FIJI software for maximum accessibility.

Computing neurite outgrowth and arborization in superior cervical ganglion neurons

Dendrites are the primary site of synaptic activity in neurons and changes in synapses are often the first pathological stage in neurodegenerative diseases. Molecular studies of these changes rely on morphological analysis of the imaging of somas and dendritic arbors of cultured or primary neurons. As research on preventing or reversing synaptic degeneration develops, demands increase for user-friendly 2D neurite analyzers without undermining accuracy and reproducibility. The most common method of 2D neurite analysis is manual by using ImageJ. This method relies completely on the user's ability to distinguish the shape and size of dendrites and trace morphology with a series of straight connected lines. Semi-automatic methods have also been developed, such as the NeuronJ plugin for ImageJ. These methods still rely on the user to identify the start and end of the dendrites, but automatically determine the shape, reducing the likelihood of user bias and speeding the process. Some automatic methods have been developed through image processing software, like ImagePro. These programs tend to be expensive, but have been shown to be fast and effective, limiting user interaction. In this study, we compare three methods of neurite analysis-ImageJ, NeuronJ, and ImagePro-in measuring the soma size, number of dendrites, and length of dendrites per cell of embryonic sympathetic rat neurons with BMP-7-induced dendritic growth. Our results indicate that ImageJ and NeuronJ measurements were of similar effectiveness and consistent throughout various images and multiple trials. NeuronJ required less user interaction in measuring the length of dendrites than the manual method and therefore, was faster and less labor intensive. Conversely, ImagePro tended to be inconsistent across images, overestimating both soma size and the number of dendrites per cell while underestimating the length of dendrites. Overall, NeuronJ, in conjunction with ImageJ, is the most reliable and efficient method of 2D neurite analysis tested in the present study.

Tomographic brain imaging with nucleolar detail and automatic cell counting

Brain tissue evaluation is essential for gaining in-depth insight into its diseases and disorders. Imaging the human brain in three dimensions has always been a challenge on the cell level. In vivo methods lack spatial resolution, and optical microscopy has a limited penetration depth. Herein, we show that hard X-ray phase tomography can visualise a volume of up to 43 mm³ of human post mortem or biopsy brain samples, by demonstrating the method on the cerebellum. We automatically identified 5,000 Purkinje cells with an error of less than 5% at their layer and determined the local surface density to 165 cells per mm² on average. Moreover, we highlight that three-dimensional data allows for the segmentation of sub-cellular structures, including dendritic tree and Purkinje cell nucleoli, without dedicated staining. The method suggests that automatic cell feature quantification of human tissues is feasible in phase tomograms obtained with isotropic resolution in a label-free manner.