Automated neurite extraction using dynamic programming for high-throughput screening of neuron-based assays

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.

NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy

One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs.

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Monitoring of cell phenotype changes by fluorescence label-free time-lapse microscopy

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Adaptive semantic segmentation for the robust detection of cell shape

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TE to correlate morphological and textural soma descriptors along time

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Directed TE graph for the representation of mutual relationship among descriptors

One of the most challenging frontiers for the automatic understanding of biological systems is fluorescent label-free imaging in which the behavior changes of living being are characterized without cell staining. To this aim, we present here the NeuriTES platform that revisits standard paradigms of video analysis to detect unlabeled objects and correlate the analysis to phenotype evolution of the mechanisms under observation. Through the exploitation of adaptive algorithms and of transfer entropy measures, the platform assures regular cell detection and the possibility to extract reliable parameters related to the evolving cell system. As a proof-of-concept, NeuriTES is applied to two fascinating phenotype investigation scenarios, amyotrophic lateral sclerosis (ALS) disease mechanism and the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Directed graphs assist the biologists with a visual understanding of the mechanisms identified.

Automated neuron tracing using probability hypothesis density filtering

Motivation

The functionality of neurons and their role in neuronal networks is tightly connected to the cell morphology. A fundamental problem in many neurobiological studies aiming to unravel this connection is the digital reconstruction of neuronal cell morphology from microscopic image data. Many methods have been developed for this, but they are far from perfect, and better methods are needed.

Results

Here we present a new method for tracing neuron centerlines needed for full reconstruction. The method uses a fundamentally different approach than previous methods by considering neuron tracing as a Bayesian multi-object tracking problem. The problem is solved using probability hypothesis density filtering. Results of experiments on 2D and 3D fluorescence microscopy image datasets of real neurons indicate the proposed method performs comparably or even better than the state of the art.

Availability and Implementation

Software implementing the proposed neuron tracing method was written in the Java programming language as a plugin for the ImageJ platform. Source code is freely available for non-commercial use at https://bitbucket.org/miroslavradojevic/phd .

Contact

meijering@imagescience.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process.

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.

NeuronCyto II: An automatic and quantitative solution for crossover neural cells in high throughput screening

Microscopy is a fundamental technology driving new biological discoveries. Today microscopy allows a large number of images to be acquired using, for example, High Throughput Screening (HTS) and 4D imaging. It is essential to be able to interrogate these images and extract quantitative information in an automated fashion. In the context of neurobiology, it is important to automatically quantify the morphology of neurons in terms of neurite number, length, branching and complexity, etc. One major issue in quantification of neuronal morphology is the “crossover” problem where neurites cross and it is difficult to assign which neurite belongs to which cell body. In the present study, we provide a solution to the “crossover” problem, the software package NeuronCyto II. NeuronCyto II is an interactive and user‐friendly software package for automatic neurite quantification. It has a well‐designed graphical user interface (GUI) with only a few free parameters allowing users to optimize the software by themselves and extract relevant quantitative information routinely. Users are able to interact with the images and the numerical features through the Result Inspector. The processing of neurites without crossover was presented in our previous work. Our solution for the “crossover” problem is developed based on our recently published work with directed graph theory. Both methods are implemented in NeuronCyto II. The results show that our solution is able to significantly improve the reliability and accuracy of the neurons displaying “crossover.” NeuronCyto II is freely available at the website: https://sites.google.com/site/neuroncyto/, which includes user support and where software upgrades will also be placed in the future. © 2016 The Authors. Cytometry Part A Published by Wiley Periodicals, Inc. on behalf of ISAC.

Imaging of the 3D dynamics of flagellar beating in human sperm

The study of the mechanical and environmental factors that regulate a fundamental event such as fertilization have been subject of multiple studies. Nevertheless, the microscopical size of the spermatozoa and the high beating frequency of their flagella (up to 20 Hz) impose a series of technological challenges for the study of the mechanical factors implicated. Traditionally, due to the inherent characteristics of the rapid sperm movement, and to the technological limitations of microscopes (optical or confocal) to follow in three dimensions (3D) their movement, the analysis of their dynamics has been studied in two dimensions, when the head is confined to a surface. Flagella propel sperm and while their head can be confined to a surface, flagellar movement is not restricted to 2D, always displaying 3D components. In this work, we present a highly novel and useful tool to analyze sperm flagella dynamics in 3D. The basis of the method is a 100 Hz oscillating objective mounted on a bright field optical microscope covering a 16 microns depth space at a rate of ~ 5000 images per second. The best flagellum focused subregions were associated to their respective Z real 3D position. Unprecedented graphical results making evident the 3D movement of the flagella are shown in this work and supplemental material illustrating a 3D animation using the obtained experimental results is also included.

Automatic Dendritic Length Quantification for High Throughput Screening of Mature Neurons

High-throughput automated fluorescent imaging and screening are important for studying neuronal development, functions, and pathogenesis. An automatic approach of analyzing images acquired in automated fashion, and quantifying dendritic characteristics is critical for making such screens high-throughput. However, automatic and effective algorithms and tools, especially for the images of mature mammalian neurons with complex arbors, have been lacking. Here, we present algorithms and a tool for quantifying dendritic length that is fundamental for analyzing growth of neuronal network. We employ a divide-and-conquer framework that tackles the challenges of high-throughput images of neurons and enables the integration of multiple automatic algorithms. Within this framework, we developed algorithms that adapt to local properties to detect faint branches. We also developed a path search that can preserve the curvature change to accurately measure dendritic length with arbor branches and turns. In addition, we proposed an ensemble strategy of three estimation algorithms to further improve the overall efficacy. We tested our tool on images for cultured mouse hippocampal neurons immunostained with a dendritic marker for high-throughput screen. Results demonstrate the effectiveness of our proposed method when comparing the accuracy with previous methods. The software has been implemented as an ImageJ plugin and available for use.

Automated computation of arbor densities: a step toward identifying neuronal cell types

The shape and position of a neuron convey information regarding its molecular and functional identity. The identification of cell types from structure, a classic method, relies on the time-consuming step of arbor tracing. However, as genetic tools and imaging methods make data-driven approaches to neuronal circuit analysis feasible, the need for automated processing increases. Here, we first establish that mouse retinal ganglion cell types can be as precise about distributing their arbor volumes across the inner plexiform layer as they are about distributing the skeletons of the arbors. Then, we describe an automated approach to computing the spatial distribution of the dendritic arbors, or arbor density, with respect to a global depth coordinate based on this observation. Our method involves three-dimensional reconstruction of neuronal arbors by a supervised machine learning algorithm, post-processing of the enhanced stacks to remove somata and isolate the neuron of interest, and registration of neurons to each other using automatically detected arbors of the starburst amacrine interneurons as fiducial markers. In principle, this method could be generalizable to other structures of the CNS, provided that they allow sparse labeling of the cells and contain a reliable axis of spatial reference.

Active learning of neuron morphology for accurate automated tracing of neurites

Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users.

Surface-printed microdot array chips for the quantification of axonal collateral branching of a single neuron in vitro

Precise and quantitative control of extracellular signalling cues using surface-engineered chips has facilitated various neurobiological assays in vitro. Although the formation of axon collateral branches is important for the establishment and refinement of the neuronal connections during the development and regeneration, surface designs for controlling branch phenotypes have been rarely proposed. In this work, we fabricated a surface-printed microdot array for controlling axon branch formation. Following the culture of hippocampal neurons on a 5 μm dot array patterned by micro-contact printing of poly-d-lysine, we found that most axon collateral branches were initiated from axonal regions on a microdot and terminated on neighboring dots. In addition, the length of branches increased as the spacing between dots increased. Surprisingly, other morphological features were not significantly different from the neurons cultured on a conventional unpatterned surface. Further investigation of this phenomenon indicated that the branch-forming machineries, such as actin patches, were focused on the dot. According to these investigations, we concluded that discontinuous adhesion spots given by dot arrays arranged the branching formation on the expectable location and direction. Therefore, microdot arrays will be applicable as the surface design parameter of bio-chip platforms to reduce branching complexity and quantize branching formation for the simple and easy assay in neurobiological studies.

Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling

Digital reconstruction of three-dimensional (3D) neuronal morphology from light microscopy images provides a powerful technique for analysis of neural circuits. It is time-consuming to manually perform this process. Thus, efficient computer-assisted approaches are preferable. In this paper, we present an innovative method for the tracing and reconstruction of 3D neuronal morphology from light microscopy images. The method uses a prediction and refinement strategy that is based on exploration of local neuron structural features. We extended the rayburst sampling algorithm to a marching fashion, which starts from a single or a few seed points and marches recursively forward along neurite branches to trace and reconstruct the whole tree-like structure. A local radius-related but size-independent hemispherical sampling was used to predict the neurite centerline and detect branches. Iterative rayburst sampling was performed in the orthogonal plane, to refine the centerline location and to estimate the local radius. We implemented the method in a cooperative 3D interactive visualization-assisted system named flNeuronTool. The source code in C++ and the binaries are freely available at http://sourceforge.net/projects/flneurontool/. We validated and evaluated the proposed method using synthetic data and real datasets from the Digital Reconstruction of Axonal and Dendritic Morphology (DIADEM) challenge. Then, flNeuronTool was applied to mouse brain images acquired with the Micro-Optical Sectioning Tomography (MOST) system, to reconstruct single neurons and local neural circuits. The results showed that the system achieves a reasonable balance between fast speed and acceptable accuracy, which is promising for interactive applications in neuronal image analysis.

APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree

MOTIVATION

Tracing of neuron morphology is an essential technique in computational neuroscience. However, despite a number of existing methods, few open-source techniques are completely or sufficiently automated and at the same time are able to generate robust results for real 3D microscopy images.

RESULTS

We developed all-path-pruning 2.0 (APP2) for 3D neuron tracing. The most important idea is to prune an initial reconstruction tree of a neuron's morphology using a long-segment-first hierarchical procedure instead of the original termini-first-search process in APP. To further enhance the robustness of APP2, we compute the distance transform of all image voxels directly for a gray-scale image, without the need to binarize the image before invoking the conventional distance transform. We also design a fast-marching algorithm-based method to compute the initial reconstruction trees without pre-computing a large graph. This method allows us to trace large images. We bench-tested APP2 on ~700 3D microscopic images and found that APP2 can generate more satisfactory results in most cases than several previous methods.

AVAILABILITY

The software has been implemented as an open-source Vaa3D plugin. The source code is available in the Vaa3D code repository http://vaa3d.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Neurient: an algorithm for automatic tracing of confluent neuronal images to determine alignment

A goal of neural tissue engineering is the development and evaluation of materials that guide neuronal growth and alignment. However, the methods available to quantitatively evaluate the response of neurons to guidance materials are limited and/or expensive, and may require manual tracing to be performed by the researcher. We have developed an open source, automated Matlab-based algorithm, building on previously published methods, to trace and quantify alignment of fluorescent images of neurons in culture. The algorithm is divided into three phases, including computation of a lookup table which contains directional information for each image, location of a set of seed points which may lie along neurite centerlines, and tracing neurites starting with each seed point and indexing into the lookup table. This method was used to obtain quantitative alignment data for complex images of densely cultured neurons. Complete automation of tracing allows for unsupervised processing of large numbers of images. Following image processing with our algorithm, available metrics to quantify neurite alignment include angular histograms, percent of neurite segments in a given direction, and mean neurite angle. The alignment information obtained from traced images can be used to compare the response of neurons to a range of conditions. This tracing algorithm is freely available to the scientific community under the name Neurient, and its implementation in Matlab allows a wide range of researchers to use a standardized, open source method to quantitatively evaluate the alignment of dense neuronal cultures.

High-throughput computer method for 3D neuronal structure reconstruction from the image stack of the Drosophila brain and its applications

Drosophila melanogaster is a well-studied model organism, especially in the field of neurophysiology and neural circuits. The brain of the Drosophila is small but complex, and the image of a single neuron in the brain can be acquired using confocal microscopy. Analyzing the Drosophila brain is an ideal start to understanding the neural structure. The most fundamental task in studying the neural network of Drosophila is to reconstruct neuronal structures from image stacks. Although the fruit fly brain is small, it contains approximately 100 000 neurons. It is impossible to trace all the neurons manually. This study presents a high-throughput algorithm for reconstructing the neuronal structures from 3D image stacks collected by a laser scanning confocal microscope. The proposed method reconstructs the neuronal structure by applying the shortest path graph algorithm. The vertices in the graph are certain points on the 2D skeletons of the neuron in the slices. These points are close to the 3D centerlines of the neuron branches. The accuracy of the algorithm was verified using the DIADEM data set. This method has been adopted as part of the protocol of the FlyCircuit Database, and was successfully applied to process more than 16 000 neurons. This study also shows that further analysis based on the reconstruction results can be performed to gather more information on the neural network.

It is now possible to image a single neuron in the fruit fly brain. However, manually reconstructing neuronal structures is tremendously time consuming. The proposed method avoids user interventions by first automatically identifying the end points and detecting the appropriate representative point of the soma, and then, by finding the shortest paths from the soma to the end points in an image stack. In the proposed algorithm, a tailor-made weighting function allows the resulting reconstruction to represent the neuron appropriately. Accuracy analysis and a robustness test demonstrated that the proposed method is accurate and robust to handle the noisy image data. Tract discovery is one of the most frequently mentioned potentials of reconstructed results. In addition to a method for neuronal structure reconstruction, this study presents a method for tract discovery and explores the tract-connecting olfactory neuropils using the reconstructed results. The discovered tracts are in agreement with the results of previous studies in the literature. Software for reconstructing the neuronal structures and the reconstruction results can be downloaded from the Web site http://www.flycircuit.tw. More details on acquiring the software and the reconstruction results are provided in Text S1.

An automated computational framework for retinal vascular network labeling and branching order analysis

Changes in retinal vascular morphology are well known as predictive clinical signs of many diseases such as hypertension, diabetes and so on. Computer-aid image processing and analysis for retinal vessels in fundus images are effective and efficient in clinical diagnosis instead of tedious manual labeling and measurement. An automated computational framework for retinal vascular network labeling and analysis is presented in this work. The framework includes 1) detecting and locating the optic disc; 2) tracking the vessel centerline from detected seed points and linking the breaks after tracing; 3) extracting all the retinal vascular trees and identifying all the significant points; and 4) classifying terminal points into starting points and ending points based on the information of optic disc location, and finally assigning branch order for each extracted vascular tree in the image. All the modules in the framework are fully automated. Based on the results, morphological analysis is then applied to achieve geometrical and topological features based on branching order for one individual vascular tree or for the vascular network through the retinal vascular network in the images. Validation and experiments on the public DRIVE database have demonstrated that the proposed framework is a novel approach to analyze and study the vascular network pattern, and may offer new insights to the diagnosis of retinopathy.

Automated reconstruction of neuronal morphology based on local geometrical and global structural models

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets.

Neuromantic - from semi-manual to semi-automatic reconstruction of neuron morphology

The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.

Automatic 3D neuron tracing using all-path pruning

Motivation: Digital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable.

Results: We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal-covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly).

Availability: The software is available upon request. We plan to eventually release the software as a plugin of the V3D-Neuron package at http://penglab.janelia.org/proj/v3d.

Contact:pengh@janelia.hhmi.org

An integrated genetics approach for identifying protein signal pathways of Alzheimer's disease

Alzheimer's disease (AD) is considered one of the most common age-associated neurodegenerative disorders, affecting millions of senior people worldwide. Combination of protein-protein interaction (PPI) network analysis and gene expression studies provides a better insight into AD. A computational approach was developed in our work to identify protein signal pathways between amyloid precursor proteins and tau proteins, which are well known as important proteins for AD. First, a modified LA-SEN method, called the network-constrained regularisation analysis, was applied to microarray data from a transgenic mouse model and AD patients. Then protein pathways were constructed based on an integer linear programming model to integrate microarray data and the PPI database. Important pathways of AD, including some cancer-related pathways, were identified finally.

NeurphologyJ: an automatic neuronal morphology quantification method and its application in pharmacological discovery

Background

Automatic quantification of neuronal morphology from images of fluorescence microscopy plays an increasingly important role in high-content screenings. However, there exist very few freeware tools and methods which provide automatic neuronal morphology quantification for pharmacological discovery.

Results

This study proposes an effective quantification method, called NeurphologyJ, capable of automatically quantifying neuronal morphologies such as soma number and size, neurite length, and neurite branching complexity (which is highly related to the numbers of attachment points and ending points). NeurphologyJ is implemented as a plugin to ImageJ, an open-source Java-based image processing and analysis platform. The high performance of NeurphologyJ arises mainly from an elegant image enhancement method. Consequently, some morphology operations of image processing can be efficiently applied. We evaluated NeurphologyJ by comparing it with both the computer-aided manual tracing method NeuronJ and an existing ImageJ-based plugin method NeuriteTracer. Our results reveal that NeurphologyJ is comparable to NeuronJ, that the coefficient correlation between the estimated neurite lengths is as high as 0.992. NeurphologyJ can accurately measure neurite length, soma number, neurite attachment points, and neurite ending points from a single image. Furthermore, the quantification result of nocodazole perturbation is consistent with its known inhibitory effect on neurite outgrowth. We were also able to calculate the IC50 of nocodazole using NeurphologyJ. This reveals that NeurphologyJ is effective enough to be utilized in applications of pharmacological discoveries.

Conclusions

This study proposes an automatic and fast neuronal quantification method NeurphologyJ. The ImageJ plugin with supports of batch processing is easily customized for dealing with high-content screening applications. The source codes of NeurphologyJ (interactive and high-throughput versions) and the images used for testing are freely available (see Availability).

Anisotropic path searching for automatic neuron reconstruction

Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of their functioning. We have developed a novel method capable of automatically tracing neurons in three-dimensional microscopy data. In contrast to template-based methods, the proposed approach makes no assumptions about the shape or appearance of neurite structure. Instead, an efficient seeding approach is applied to capture complex neuronal structures and the tracing problem is solved by computing the optimal reconstruction with a weighted graph. The optimality is determined by the cost function designed for the path between each pair of seeds and by topological constraints defining the component interrelations and completeness. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient and has been validated using different types of microscopy data sets including Drosophila's projection neurons and fly neurons with presynaptic sites. In all cases, the approach yielded promising results.

Automated reconstruction of neuronal morphology: an overview

Digital reconstruction of neuronal morphology is a powerful technique for investigating the nervous system. This process consists of tracing the axonal and dendritic arbors of neurons imaged by optical microscopy into a geometrical format suitable for quantitative analysis and computational modeling. Algorithmic automation of neuronal tracing promises to increase the speed, accuracy, and reproducibility of morphological reconstructions. Together with recent breakthroughs in cellular imaging and accelerating progress in optical microscopy, automated reconstruction of neuronal morphology will play a central role in the development of high throughput screening and the acquisition of connectomic data. Yet, despite continuous advances in image processing algorithms, to date manual tracing remains the overwhelming choice for digitizing neuronal morphology. We summarize the issues involved in automated reconstruction, overview the available techniques, and provide a realistic assessment of future perspectives.

Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model

Motivation: Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns.

Results: We developed a graph-augmented deformable model (GD) to reconstruct (trace) the 3D structure of a neuron when it has a broken structure and/or fuzzy boundary. We formulated a variational problem using the geodesic shortest path, which is defined as a combination of Euclidean distance, exponent of inverse intensity of pixels along the path and closeness to local centers of image intensity distribution. We solved it in two steps. We first used a shortest path graph algorithm to guarantee that we find the global optimal solution of this step. Then we optimized a discrete deformable curve model to achieve visually more satisfactory reconstructions. Within our framework, it is also easy to define an optional prior curve that reflects the domain knowledge of a user. We investigated the performance of our method using a number of challenging 3D neuronal image datasets of different model organisms including fruit fly, Caenorhabditis elegans, and mouse. In our experiments, the GD method outperformed several comparison methods in reconstruction accuracy, consistency, robustness and speed. We further used GD in two real applications, namely cataloging neurite morphology of fruit fly to build a 3D ‘standard’ digital neurite atlas, and estimating the synaptic bouton density along the axons for a mouse brain.

Availability: The software is provided as part of the V3D-Neuron 1.0 package freely available at http://penglab.janelia.org/proj/v3d

Contact:pengh@janelia.hhmi.org

Neuron tracing in perspective

The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. (c) 2010 International Society for Advancement of Cytometry.

Automatic robust neurite detection and morphological analysis of neuronal cell cultures in high-content screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutamine-mediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington's Disease (HD) model.

A computational framework for studying neuron morphology from in vitro high content neuron-based screening

High content neuron image processing is considered as an important method for quantitative neurobiological studies. The main goal of analysis in this paper is to provide automatic image processing approaches to process neuron images for studying neuron mechanism in high content screening. In the nuclei channel, all nuclei are segmented and detected by applying the gradient vector field based watershed. Then the neuronal nuclei are selected based on the soma region detected in neurite channel. In neurite images, we propose a novel neurite centerline extraction approach using the improved line-pixel detection technique. The proposed neurite tracing method can detect the curvilinear structure more accurately compared with the current existing methods. An interface called NeuriteIQ based on the proposed algorithms is developed finally for better application in high content screening.

V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets

The V3D system provides three-dimensional (3D) visualization of gigabyte-sized microscopy image stacks in real time on current laptops and desktops. Combined with highly ergonomic features for selecting an X, Y, Z location of an image directly in 3D space and for visualizing overlays of a variety of surface objects, V3D streamlines the on-line analysis, measurement, and proofreading of complicated image patterns. V3D is cross-platform and can be enhanced by plug-ins. We built V3D-Neuron on top of V3D to reconstruct complex 3D neuronal structures from large brain images. V3D-Neuron enables us to precisely digitize the morphology of a single neuron in a fruit fly brain in minutes, with about 17-fold improvement in reliability and 10-fold savings in time compared to other neuron reconstruction tools. Using V3D-Neuron, we demonstrated the feasibility of building a 3D digital atlas of neurite tracts in the fruit fly brain.

High-content screening of primary neurons: ready for prime time

High-content screening (HCS), historically limited to drug-development companies, is now a powerful and affordable technology for academic researchers. Through automated routines, this technology acquires large datasets of fluorescence images depicting the functional states of thousands to millions of cells. Information on shapes, textures, intensities, and localizations is then used to create unique representations, or 'phenotypic signatures,' of each cell. These signatures quantify physiologic or diseased states, for example, dendritic arborization, drug response, or cell coping strategies. Live-cell imaging in HCS adds the ability to correlate cellular events at different points in time, thereby allowing sensitivities and observations not possible with fixed endpoint analysis. HCS with live-cell imaging therefore provides an unprecedented capability to detect spatiotemporal changes in cells and is particularly suited for time-dependent, stochastic processes such as neurodegenerative disorders.

An automatic integrated approach for stained neuron detection in studying neuron migration

Neurons that come to populate the six-layered cerebral cortex are born deep within the developing brain in the surface of the embryonic cerebral ventricles. It is very important to detect these neurons for studying histogenesis of the brain and abnormal migration that had been linked to cognitive deficits, mental retardation, and motor disorders. The visualization of labeled cells in brain sections was performed by immunocytochemical examination and its image data were documented to microscopic pictures. Based on the fact, automatic accurate neurons labeling is prerequisite instead of time-consuming manual labeling. In this article, a fully automated image processing approach is proposed to detect all the stained neurons in microscopic images. First of all, dark stained neurons are achieved by thresholding in blue channel of image. And then a modified fuzzy c-means clustering method, called alternative fuzzy c-means is applied to achieve higher classification accuracy in extracting constraint factor. Finally, watershed based on gradient vector flow is employed to the constraint factor image to segment all the neurons, including clustered neurons. The results demonstrate that the proposed method can be a useful tool in neuron image analysis.

An Image Based System Biology Approach for Alzheimer's Disease Pathway Analysis

We report multifactorial analysis of candidate mechanisms of Alzheimer's disease utilizing high content analysis, gene expression microarray, and linear regression model to integrate neuronal imaging data with hippocampal gene expression data. Our analysis led to the identification of several genes that may contribute to different image traits or phenotypes in the amyloid-beta (Aβ) injured neurons. Gene network and biological pathways analysis for those genes were further analyzed and led to several novel pathways that may contribute to amyloid plaque triggered neurite loss.

3D Axon structure extraction and analysis in confocal fluorescence microscopy images

The morphological properties of axons, such as their branching patterns and oriented structures, are of great interest for biologists in the study of the synaptic connectivity of neurons. In these studies, researchers use triple immunofluorescent confocal microscopy to record morphological changes of neuronal processes. Three-dimensional (3D) microscopy image analysis is then required to extract morphological features of the neuronal structures. In this article, we propose a highly automated 3D centerline extraction tool to assist in this task. For this project, the most difficult part is that some axons are overlapping such that the boundaries distinguishing them are barely visible. Our approach combines a 3D dynamic programming (DP) technique and marker-controlled watershed algorithm to solve this problem. The approach consists of tracking and updating along the navigation directions of multiple axons simultaneously. The experimental results show that the proposed method can rapidly and accurately extract multiple axon centerlines and can handle complicated axon structures such as cross-over sections and overlapping objects.