Improved Automatic Centerline Tracing for Dendritic and Axonal Structures

Neuron tracing from light microscopy images: automation, deep learning and bench testing

MOTIVATION

Large-scale neuronal morphologies are essential to neuronal typing, connectivity characterization and brain modeling. It is widely accepted that automation is critical to the production of neuronal morphology. Despite previous survey papers about neuron tracing from light microscopy data in the last decade, thanks to the rapid development of the field, there is a need to update recent progress in a review focusing on new methods and remarkable applications.

RESULTS

This review outlines neuron tracing in various scenarios with the goal to help the community understand and navigate tools and resources. We describe the status, examples and accessibility of automatic neuron tracing. We survey recent advances of the increasingly popular deep-learning enhanced methods. We highlight the semi-automatic methods for single neuron tracing of mammalian whole brains as well as the resulting datasets, each containing thousands of full neuron morphologies. Finally, we exemplify the commonly used datasets and metrics for neuron tracing bench testing.

Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation

Microscopic images of neuronal cells provide essential structural information about the key constituents of the brain and form the basis of many neuroscientific studies. Computational analyses of the morphological properties of the captured neurons require first converting the structural information into digital tree-like reconstructions. Many dedicated computational methods and corresponding software tools have been and are continuously being developed with the aim to automate this step while achieving human-comparable reconstruction accuracy. This pursuit is hampered by the immense diversity and intricacy of neuronal morphologies as well as the often low quality and ambiguity of the images. Here we present a novel method we developed in an effort to improve the robustness of digital reconstruction against these complicating factors. The method is based on probabilistic filtering by sequential Monte Carlo estimation and uses prediction and update models designed specifically for tracing neuronal branches in microscopic image stacks. Moreover, it uses multiple probabilistic traces to arrive at a more robust, ensemble reconstruction. The proposed method was evaluated on fluorescence microscopy image stacks of single neurons and dense neuronal networks with expert manual annotations serving as the gold standard, as well as on synthetic images with known ground truth. The results indicate that our method performs well under varying experimental conditions and compares favorably to state-of-the-art alternative methods.

3-D Human Sperm Flagellum Tracing in Low SNR Fluorescence Images

Tracing tubular structures from biomedical images is important for a wide range of applications. Particularly, the spermatozoon is an essential cell whose flagella have a tubular form. Its main function is to fertilize the egg, and the flagellum is fundamental to achieve this task which depends importantly on the dynamics of intracellular calcium ([Ca²⁺]_i). Measuring [Ca²⁺]_i along the flagellum in 3-D is not a simple matter since it requires: 1) sophisticated fluorescence imaging techniques dealing with low intensity and signal to noise ratio (SNR) and 2) tracing the flagellum's centerline. Most of the algorithms proposed to trace tubular structures have been developed for multi-branch structures not being adequate for single tubular structures with low SNR. Taking into account the prior knowledge that the flagellum is constituted by a single tubular structure, we propose an automatic method to trace and track multiple single tubular structures from 3-D images. First, an algorithm based on one-class classification allows enhancement of the flagellum. This enhanced 3-D image permits guiding an iterative centerline algorithm toward the flagellum's centerline. Each sperm is assigned an ID to keep track of it in 3-D . Our algorithm was quantitatively evaluated using a ground truth 564 semi-manual traces (six 3-D image stacks) comparing them to those obtained from state-of-the-art tubular structure centerline extraction algorithms. The qualitative and quantitative results show that our algorithm is extracting similar traces as compared with ground truth, and it is more robust and accurate to trace the flagellum's centerline than multi-branch algorithms.

Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer

Fluorescence confocal microscopy has become increasingly more important in neuroscience due to its applications in image-based screening and profiling of neurons. Multispectral confocal imaging is useful to simultaneously probe for distribution of multiple analytes over networks of neurons. However, current automated image analysis algorithms are not designed to extract single-neuron arbors in images where neurons are not separated, hampering the ability map fluorescence signals at the single cell level. To overcome this limitation, we introduce NeuroTreeTracer - a novel image processing framework aimed at automatically extracting and sorting single-neuron traces in fluorescent images of multicellular neuronal networks. This method applies directional multiscale filters for automated segmentation of neurons and soma detection, and includes a novel tracing routine that sorts neuronal trees in the image by resolving network connectivity even when neurites appear to intersect. By extracting each neuronal tree, NeuroTreetracer enables to automatically quantify the spatial distribution of analytes of interest in the subcellular compartments of individual neurons. This software is released open-source and freely available with the goal to facilitate applications in neuron screening and profiling.

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.

Improved detection of soma location and morphology in fluorescence microscopy images of neurons

BACKGROUND

Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures.

NEW METHOD

In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution.

RESULTS

Extensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones.

COMPARISON WITH EXISTING METHODS

We include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency.

CONCLUSIONS

Our algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community.

Multiscale Analysis of Neurite Orientation and Spatial Organization in Neuronal Images

The spatial organization of neurites, the thin processes (i.e., dendrites and axons) that stem from a neuron's soma, conveys structural information required for proper brain function. The alignment, direction and overall geometry of neurites in the brain are subject to continuous remodeling in response to healthy and noxious stimuli. In the developing brain, during neurogenesis or in neuroregeneration, these structural changes are indicators of the ability of neurons to establish axon-to-dendrite connections that can ultimately develop into functional synapses. Enabling a proper quantification of this structural remodeling would facilitate the identification of new phenotypic criteria to classify developmental stages and further our understanding of brain function. However, adequate algorithms to accurately and reliably quantify neurite orientation and alignment are still lacking. To fill this gap, we introduce a novel algorithm that relies on multiscale directional filters designed to measure local neurites orientation over multiple scales. This innovative approach allows us to discriminate the physical orientation of neurites from finer scale phenomena associated with local irregularities and noise. Building on this multiscale framework, we also introduce a notion of alignment score that we apply to quantify the degree of spatial organization of neurites in tissue and cultured neurons. Numerical codes were implemented in Python and released open source and freely available to the scientific community.

Genetic deletion of fibroblast growth factor 14 recapitulates phenotypic alterations underlying cognitive impairment associated with schizophrenia

Cognitive processing is highly dependent on the functional integrity of gamma-amino-butyric acid (GABA) interneurons in the brain. These cells regulate excitability and synaptic plasticity of principal neurons balancing the excitatory/inhibitory tone of cortical networks. Reduced function of parvalbumin (PV) interneurons and disruption of GABAergic synapses in the cortical circuitry result in desynchronized network activity associated with cognitive impairment across many psychiatric disorders, including schizophrenia. However, the mechanisms underlying these complex phenotypes are still poorly understood. Here we show that in animal models, genetic deletion of fibroblast growth factor 14 (Fgf14), a regulator of neuronal excitability and synaptic transmission, leads to loss of PV interneurons in the CA1 hippocampal region, a critical area for cognitive function. Strikingly, this cellular phenotype associates with decreased expression of glutamic acid decarboxylase 67 (GAD67) and vesicular GABA transporter (VGAT) and also coincides with disrupted CA1 inhibitory circuitry, reduced in vivo gamma frequency oscillations and impaired working memory. Bioinformatics analysis of schizophrenia transcriptomics revealed functional co-clustering of FGF14 and genes enriched within the GABAergic pathway along with correlatively decreased expression of FGF14, PVALB, GAD67 and VGAT in the disease context. These results indicate that Fgf14(-/-) mice recapitulate salient molecular, cellular, functional and behavioral features associated with human cognitive impairment, and FGF14 loss of function might be associated with the biology of complex brain disorders such as schizophrenia.

Multi-scale segmentation of neurons based on one-class classification

BACKGROUND

High resolution multiphoton and confocal microscopy has allowed the acquisition of large amounts of data to be analyzed by neuroscientists. However, manual processing of these images has become infeasible. Thus, there is a need to create automatic methods for the morphological reconstruction of 3D neuronal image stacks.

NEW METHOD

An algorithm to extract the 3D morphology from a neuron is presented. The main contribution of the paper is the segmentation of the neuron from the background. Our segmentation method is based on one-class classification where the 3D image stack is analyzed at different scales. First, a multi-scale approach is proposed to compute the Laplacian of the 3D image stack. The Laplacian is used to select a training set consisting of background points. A decision function is learned for each scale from the training set that allows determining how similar an unlabeled point is to the points in the background class. Foreground points (dendrites and axons) are assigned as those points that are rejected as background. Finally, the morphological reconstruction of the neuron is extracted by applying a state-of-the-art centerline tracing algorithm on the segmentation.

RESULTS

Quantitative and qualitative results on several datasets demonstrate the ability of our algorithm to accurately and robustly segment and trace neurons.

COMPARISON WITH EXISTING METHOD(S)

Our method was compared to state-of-the-art neuron tracing algorithms.

CONCLUSIONS

Our approach allows segmentation of thin and low contrast dendrites that are usually difficult to segment. Compared to our previous approach, this algorithm is more accurate and much faster.

Automated detection of soma location and morphology in neuronal network cultures

Automated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i) image segmentation based on support vector machines with specially designed multiscale filters; (ii) soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma's surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.

Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors

MOTIVATION

The arbor morphologies of brain microglia are important indicators of cell activation. This article fills the need for accurate, robust, adaptive and scalable methods for reconstructing 3-D microglial arbors and quantitatively mapping microglia activation states over extended brain tissue regions.

RESULTS

Thick rat brain sections (100-300 µm) were multiplex immunolabeled for IBA1 and Hoechst, and imaged by step-and-image confocal microscopy with automated 3-D image mosaicing, producing seamless images of extended brain regions (e.g. 5903 × 9874 × 229 voxels). An over-complete dictionary-based model was learned for the image-specific local structure of microglial processes. The microglial arbors were reconstructed seamlessly using an automated and scalable algorithm that exploits microglia-specific constraints. This method detected 80.1 and 92.8% more centered arbor points, and 53.5 and 55.5% fewer spurious points than existing vesselness and LoG-based methods, respectively, and the traces were 13.1 and 15.5% more accurate based on the DIADEM metric. The arbor morphologies were quantified using Scorcioni's L-measure. Coifman's harmonic co-clustering revealed four morphologically distinct classes that concord with known microglia activation patterns. This enabled us to map spatial distributions of microglial activation and cell abundances.

AVAILABILITY AND IMPLEMENTATION

Experimental protocols, sample datasets, scalable open-source multi-threaded software implementation (C++, MATLAB) in the electronic supplement, and website (www.farsight-toolkit.org). http://www.farsight-toolkit.org/wiki/Population-scale_Three-dimensional_Reconstruction_and_Quanti-tative_Profiling_of_Microglia_Arbors

CONTACT

broysam@central.uh.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.