A Manual Segmentation Tool for Three-Dimensional Neuron Datasets

Automated Analysis of Neuronal Morphology in 2D Fluorescence Micrographs through an Unsupervised Semantic Segmentation of Neurons

Brain function emerges from a highly complex network of specialized cells that are interlinked by billions of synapses. The synaptic connectivity between neurons is established between the elongated processes of their axons and dendrites or, together, neurites. To establish these connections, cellular neurites have to grow in highly specialized, cell-type dependent patterns covering extensive distances and connecting with thousands of other neurons. The outgrowth and branching of neurites are tightly controlled during development and are a commonly used functional readout of imaging in the neurosciences. Manual analysis of neuronal morphology from microscopy images, however, is very time intensive and prone to bias. Most automated analyses of neurons rely on reconstruction of the neuron as a whole without a semantic analysis of each neurite. A fully-automated classification of all neurites still remains unavailable in open-source software. Here we present a standalone, GUI-based software for batch-quantification of neuronal morphology in two-dimensional fluorescence micrographs of cultured neurons with minimal requirements for user interaction. Single neurons are first reconstructed into binarized images using a Hessian-based segmentation algorithm to detect thin neurite structures combined with intensity- and shape-based reconstruction of the cell body. Neurites are then classified into axon, dendrites and their branches of increasing order using a geodesic distance transform of the cell skeleton. The software was benchmarked against a published dataset and reproduced the phenotype observed after manual annotation. Our tool promises accelerated and improved morphometric studies of neuronal morphology by allowing for consistent and automated analysis of large datasets.

A modular framework for multi-scale tissue imaging and neuronal segmentation

The development of robust tools for segmenting cellular and sub-cellular neuronal structures lags behind the massive production of high-resolution 3D images of neurons in brain tissue. The challenges are principally related to high neuronal density and low signal-to-noise characteristics in thick samples, as well as the heterogeneity of data acquired with different imaging methods. To address this issue, we design a framework which includes sample preparation for high resolution imaging and image analysis. Specifically, we set up a method for labeling thick samples and develop SENPAI, a scalable algorithm for segmenting neurons at cellular and sub-cellular scales in conventional and super-resolution STimulated Emission Depletion (STED) microscopy images of brain tissues. Further, we propose a validation paradigm for testing segmentation performance when a manual ground-truth may not exhaustively describe neuronal arborization. We show that SENPAI provides accurate multi-scale segmentation, from entire neurons down to spines, outperforming state-of-the-art tools. The framework will empower image processing of complex neuronal circuitries.

A sense of proximity: Cell packing modulates oxygen consumption

Accurately modeling oxygen transport and consumption is crucial to predict metabolic dynamics in cell cultures and optimize the design of tissue and organ models. We present a methodology to characterize the Michaelis-Menten oxygen consumption parameters in vitro, integrating novel experimental techniques and computational tools. The parameters were derived for hepatic cell cultures with different dimensionality (i.e., 2D and 3D) and with different surface and volumetric densities. To quantify cell packing regardless of the dimensionality of cultures, we devised an image-based metric, referred to as the proximity index. The Michaelis-Menten parameters were related to the proximity index through an uptake coefficient, analogous to a diffusion constant, enabling the quantitative analysis of oxygen dynamics across dimensions. Our results show that Michaelis-Menten parameters are not constant for a given cell type but change with dimensionality and cell density. The maximum consumption rate per cell decreases significantly with cell surface and volumetric density, while the Michaelis-Menten constant tends to increase. In addition, the dependency of the uptake coefficient on the proximity index suggests that the oxygen consumption rate of hepatic cells is superadaptive, as they modulate their oxygen utilization according to its local availability and to the proximity of other cells. We describe, for the first time, how cells consume oxygen as a function of cell proximity, through a quantitative index, which combines cell density and dimensionality. This study enhances our understanding of how cell-cell interaction affects oxygen dynamics and enables better prediction of aerobic metabolism in tissue models, improving their translational value.

NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images

We introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-field microscopy. NeuroConstruct offers a Segmentation Toolbox with various annotation helper functions that aid experts to effectively and precisely annotate micrometer resolution neurites. It also offers an automatic neurites segmentation using convolutional neuronal networks (CNN) trained by the Toolbox annotations and somas segmentation using thresholding. To visualize neurites in a given volume, NeuroConstruct offers a hybrid rendering by combining iso-surface rendering of high-confidence classified neurites, along with real-time rendering of raw volume using a 2D transfer function for voxel classification score versus voxel intensity value. For a complete reconstruction of the 3D neurites, we introduce a Registration Toolbox that provides automatic coarse-to-fine alignment of serially sectioned samples. The quantitative and qualitative analysis show that NeuroConstruct outperforms the state-of-the-art in all design aspects. NeuroConstruct was developed as a collaboration between computer scientists and neuroscientists, with an application to the study of cholinergic neurons, which are severely affected in Alzheimer's disease.

Neuron segmentation using 3D wavelet integrated encoder-decoder network

MOTIVATION

3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task.

RESULTS

In this article, we propose a 3D wavelet and deep learning-based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segmentation task. Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes; the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the biggest available annotated neuronal image dataset, BigNeuron, to train 3D WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to generate the complete neuron, which is digitally reconstructed using an available automatic tracing algorithm. The experimental results show that our neuron segmentation method could completely extract the target neuron in noisy neuronal images. The integrated 3D wavelets can efficiently improve the performance of 3D neuron segmentation and reconstruction.

AVAILABILITYAND IMPLEMENTATION

The data and codes for this work are available at https://github.com/LiQiufu/3D-WaveUNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

NeuroRetriever: Automatic Neuron Segmentation for Connectome Assembly

Segmenting individual neurons from a large number of noisy raw images is the first step in building a comprehensive map of neuron-to-neuron connections for predicting information flow in the brain. Thousands of fluorescence-labeled brain neurons have been imaged. However, mapping a complete connectome remains challenging because imaged neurons are often entangled and manual segmentation of a large population of single neurons is laborious and prone to bias. In this study, we report an automatic algorithm, NeuroRetriever, for unbiased large-scale segmentation of confocal fluorescence images of single neurons in the adult Drosophila brain. NeuroRetriever uses a high-dynamic-range thresholding method to segment three-dimensional morphology of single neurons based on branch-specific structural features. Applying NeuroRetriever to automatically segment single neurons in 22,037 raw brain images, we successfully retrieved 28,125 individual neurons validated by human segmentation. Thus, automated NeuroRetriever will greatly accelerate 3D reconstruction of the single neurons for constructing the complete connectomes.

GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population

Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem. GTree offers an error-screening system for the fast localization of submicron errors in densely packed neurites and along with long projections across the whole brain, thus achieving reconstruction close to the ground truth. Moreover, GTree integrates a series of our previous algorithms to significantly reduce manual interference and achieve high-level automation. When applied to an entire mouse brain dataset, GTree is shown to be five times faster than widely used commercial software. Finally, using GTree, we demonstrate the reconstruction of 35 long-projection neurons around one injection site of a mouse brain. GTree is also applicable to large datasets (10 TB or higher) from various light microscopes.

REAVER: A program for improved analysis of high-resolution vascular network images

Alterations in vascular networks, including angiogenesis and capillary regression, play key roles in disease, wound healing, and development. The spatial structures of blood vessels can be captured through imaging, but effective characterization of network architecture requires both metrics for quantification and software to carry out the analysis in a high‐throughput and unbiased fashion. We present Rapid Editable Analysis of Vessel Elements Routine (REAVER), an open‐source tool that researchers can use to analyze high‐resolution 2D fluorescent images of blood vessel networks, and assess its performance compared to alternative image analysis programs. Using a dataset of manually analyzed images from a variety of murine tissues as a ground‐truth, REAVER exhibited high accuracy and precision for all vessel architecture metrics quantified, including vessel length density, vessel area fraction, mean vessel diameter, and branchpoint count, along with the highest pixel‐by‐pixel accuracy for the segmentation of the blood vessel network. In instances where REAVER's automated segmentation is inaccurate, we show that combining manual curation with automated analysis improves the accuracy of vessel architecture metrics. REAVER can be used to quantify differences in blood vessel architectures, making it useful in experiments designed to evaluate the effects of different external perturbations (eg, drugs or disease states).

A Smart Region-Growing Algorithm for Single-Neuron Segmentation From Confocal and 2-Photon Datasets

Accurately digitizing the brain at the micro-scale is crucial for investigating brain structure-function relationships and documenting morphological alterations due to neuropathies. Here we present a new Smart Region Growing algorithm (SmRG) for the segmentation of single neurons in their intricate 3D arrangement within the brain. Its Region Growing procedure is based on a homogeneity predicate determined by describing the pixel intensity statistics of confocal acquisitions with a mixture model, enabling an accurate reconstruction of complex 3D cellular structures from high-resolution images of neural tissue. The algorithm's outcome is a 3D matrix of logical values identifying the voxels belonging to the segmented structure, thus providing additional useful volumetric information on neurons. To highlight the algorithm's full potential, we compared its performance in terms of accuracy, reproducibility, precision and robustness of 3D neuron reconstructions based on microscopic data from different brain locations and imaging protocols against both manual and state-of-the-art reconstruction tools.

Clearing techniques for visualizing the nervous system in development, injury, and disease

Modern clearing techniques enable high resolution visualization and 3D reconstruction of cell populations and their structural details throughout large biological samples, including intact organs and even entire organisms. In the past decade, these methods have become more tractable and are now being utilized to provide unforeseen insights into the complexities of the nervous system. While several iterations of optical clearing techniques have been developed, some are more suitable for specific applications than others depending on the type of specimen under study. Here we review findings from select studies utilizing clearing methods to visualize the developing, injured, and diseased nervous system within numerous model systems and species. We note trends and imbalances in the types of research questions being addressed with clearing methods across these fields in neuroscience. In addition, we discuss restrictions in applying optical clearing methods for postmortem tissue from humans and large animals and emphasize the lack in continuity between studies of these species. We aim for this review to serve as a key outline of available tissue clearing methods used successfully to address issues across neuronal development, injury/repair, and aging/disease.

Gotta Trace 'em All: A Mini-Review on Tools and Procedures for Segmenting Single Neurons Toward Deciphering the Structural Connectome

Decoding the morphology and physical connections of all the neurons populating a brain is necessary for predicting and studying the relationships between its form and function, as well as for documenting structural abnormalities in neuropathies. Digitizing a complete and high-fidelity map of the mammalian brain at the micro-scale will allow neuroscientists to understand disease, consciousness, and ultimately what it is that makes us humans. The critical obstacle for reaching this goal is the lack of robust and accurate tools able to deal with 3D datasets representing dense-packed cells in their native arrangement within the brain. This obliges neuroscientist to manually identify the neurons populating an acquired digital image stack, a notably time-consuming procedure prone to human bias. Here we review the automatic and semi-automatic algorithms and software for neuron segmentation available in the literature, as well as the metrics purposely designed for their validation, highlighting their strengths and limitations. In this direction, we also briefly introduce the recent advances in tissue clarification that enable significant improvements in both optical access of neural tissue and image stack quality, and which could enable more efficient segmentation approaches. Finally, we discuss new methods and tools for processing tissues and acquiring images at sub-cellular scales, which will require new robust algorithms for identifying neurons and their sub-structures (e.g., spines, thin neurites). This will lead to a more detailed structural map of the brain, taking twenty-first century cellular neuroscience to the next level, i.e., the Structural Connectome.

Hydrogel-Tissue Chemistry: Principles and Applications

Over the past five years, a rapidly developing experimental approach has enabled high-resolution and high-content information retrieval from intact multicellular animal (metazoan) systems. New chemical and physical forms are created in the hydrogel-tissue chemistry process, and the retention and retrieval of crucial phenotypic information regarding constituent cells and molecules (and their joint interrelationships) are thereby enabled. For example, rich data sets defining both single-cell-resolution gene expression and single-cell-resolution activity during behavior can now be collected while still preserving information on three-dimensional positioning and/or brain-wide wiring of those very same neurons-even within vertebrate brains. This new approach and its variants, as applied to neuroscience, are beginning to illuminate the fundamental cellular and chemical representations of sensation, cognition, and action. More generally, reimagining metazoans as metareactants-or positionally defined three-dimensional graphs of constituent chemicals made available for ongoing functionalization, transformation, and readout-is stimulating innovation across biology and medicine.

A Segmentation Scheme for Complex Neuronal Arbors and Application to Vibration Sensitive Neurons in the Honeybee Brain

The morphology of a neuron is strongly related to its physiological properties, application of logical product and thus to information processing functions. Optical microscope images are widely used for extracting the structure of neurons. Although several approaches have been proposed to trace and extract complex neuronal structures from microscopy images, available methods remain prone to errors. In this study, we present a practical scheme for processing confocal microscope images and reconstructing neuronal structures. We evaluated this scheme using image data samples and associated “gold standard” reconstructions from the BigNeuron Project. In these samples, dendritic arbors belonging to multiple projection branches of the same neuron overlapped in space, making it difficult to automatically and accurately trace their structural connectivity. Our proposed scheme, which combines several software tools for image masking and filtering with an existing tool for dendritic segmentation and tracing, outperformed state-of-the-art automatic methods in reconstructing such neuron structures. For evaluating our scheme, we applied it to a honeybee local interneuron, DL-Int-1, which has complex arbors and is considered to be a critical neuron for encoding the distance information indicated in the waggle dance of the honeybee.