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Wei X, Liu Q, Liu M, Wang Y, Meijering E. 3D Soma Detection in Large-Scale Whole Brain Images via a Two-Stage Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:148-157. [PMID: 36103445 DOI: 10.1109/tmi.2022.3206605] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
3D soma detection in whole brain images is a critical step for neuron reconstruction. However, existing soma detection methods are not suitable for whole mouse brain images with large amounts of data and complex structure. In this paper, we propose a two-stage deep neural network to achieve fast and accurate soma detection in large-scale and high-resolution whole mouse brain images (more than 1TB). For the first stage, a lightweight Multi-level Cross Classification Network (MCC-Net) is proposed to filter out images without somas and generate coarse candidate images by combining the advantages of the multi convolution layer's feature extraction ability. It can speed up the detection of somas and reduce the computational complexity. For the second stage, to further obtain the accurate locations of somas in the whole mouse brain images, the Scale Fusion Segmentation Network (SFS-Net) is developed to segment soma regions from candidate images. Specifically, the SFS-Net captures multi-scale context information and establishes a complementary relationship between encoder and decoder by combining the encoder-decoder structure and a 3D Scale-Aware Pyramid Fusion (SAPF) module for better segmentation performance. The experimental results on three whole mouse brain images verify that the proposed method can achieve excellent performance and provide the reconstruction of neurons with beneficial information. Additionally, we have established a public dataset named WBMSD, including 798 high-resolution and representative images ( 256 ×256 ×256 voxels) from three whole mouse brain images, dedicated to the research of soma detection, which will be released along with this paper.
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Liu Y, Wang G, Ascoli GA, Zhou J, Liu L. Neuron tracing from light microscopy images: automation, deep learning and bench testing. Bioinformatics 2022; 38:5329-5339. [PMID: 36303315 PMCID: PMC9750132 DOI: 10.1093/bioinformatics/btac712] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/19/2022] [Accepted: 10/26/2022] [Indexed: 12/24/2022] Open
Abstract
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.
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Affiliation(s)
- Yufeng Liu
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Gaoyu Wang
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Jiangning Zhou
- Institute of Brain Science, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Lijuan Liu
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
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Shen L, Liu M, Wang C, Guo C, Meijering E, Wang Y. Efficient 3D Junction Detection in Biomedical Images Based on a Circular Sampling Model and Reverse Mapping. IEEE J Biomed Health Inform 2021; 25:1612-1623. [PMID: 33166258 DOI: 10.1109/jbhi.2020.3036743] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Detection and localization of terminations and junctions is a key step in the morphological reconstruction of tree-like structures in images. Previously, a ray-shooting model was proposed to detect termination points automatically. In this paper, we propose an automatic method for 3D junction points detection in biomedical images, relying on a circular sampling model and a 2D-to-3D reverse mapping approach. First, the existing ray-shooting model is improved to a circular sampling model to extract the pixel intensity distribution feature across the potential branches around the point of interest. The computation cost can be reduced dramatically compared to the existing ray-shooting model. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed to detect 2D junction points in maximum intensity projections (MIPs) of sub-volume images in a given 3D image, by determining the number of branches in the candidate junction region. Further, a 2D-to-3D reverse mapping approach is used to map these detected 2D junction points in MIPs to the 3D junction points in the original 3D images. The proposed 3D junction point detection method is implemented as a build-in tool in the Vaa3D platform. Experiments on multiple 2D images and 3D images show average precision and recall rates of 87.11% and 88.33% respectively. In addition, the proposed algorithm is dozens of times faster than the existing deep-learning based model. The proposed method has excellent performance in both detection precision and computation efficiency for junction detection even in large-scale biomedical images.
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Radojević M, Meijering E. Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation. Neuroinformatics 2020; 17:423-442. [PMID: 30542954 PMCID: PMC6594993 DOI: 10.1007/s12021-018-9407-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
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.
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Affiliation(s)
- Miroslav Radojević
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
| | - Erik Meijering
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
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Tan Y, Liu M, Chen W, Wang X, Peng H, Wang Y. DeepBranch: Deep Neural Networks for Branch Point Detection in Biomedical Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1195-1205. [PMID: 31603774 DOI: 10.1109/tmi.2019.2945980] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Morphology reconstruction of tree-like structures in volumetric images, such as neurons, retinal blood vessels, and bronchi, is of fundamental interest for biomedical research. 3D branch points play an important role in many reconstruction applications, especially for graph-based or seed-based reconstruction methods and can help to visualize the morphology structures. There are a few hand-crafted models proposed to detect the branch points. However, they are highly dependent on the empirical setting of the parameters for different images. In this paper, we propose a DeepBranch model for branch point detection with two-level designed convolutional networks, a candidate region segmenter and a false positive reducer. On the first level, an improved 3D U-Net model with anisotropic convolution kernels is employed to detect initial candidates. Compared with the traditional sliding window strategy, the improved 3D U-Net can avoid massive redundant computations and dramatically speed up the detection process by employing dense-inference with fully convolutional neural networks (FCN). On the second level, a method based on multi-scale multi-view convolutional neural networks (MSMV-Net) is proposed for false positive reduction by feeding multi-scale views of 3D volumes into multiple streams of 2D convolution neural networks (CNNs), which can take full advantage of spatial contextual information as well as fit different sizes. Experiments on multiple 3D biomedical images of neurons, retinal blood vessels and bronchi confirm that the proposed 3D branch point detection method outperforms other state-of-the-art detection methods, and is helpful for graph-based or seed-based reconstruction methods.
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Jeelani H, Liang H, Acton ST, Weller DS. Content-Aware Enhancement of Images With Filamentous Structures. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 28:3451-3461. [PMID: 30716037 PMCID: PMC6538482 DOI: 10.1109/tip.2019.2897289] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
In this paper, we describe a novel enhancement method for images containing filamentous structures. Our method combines a gradient sparsity constraint with a filamentous structure constraint for the effective removal of clutter and noise from the background. The method is applied and evaluated on three types of data: 1) confocal microscopy images of neurons; 2) calcium imaging data; and 3) images of road pavement. We found that the images enhanced by our method preserve both the structure and the intensity details of the original object. In the case of neuron microscopy, we find that the neurons enhanced by our method are better correlated with the original structure intensities than the neurons enhanced by well-known vessel enhancement methods. Experiments on simulated calcium imaging data indicate that both the number of detected neurons and the accuracy of the derived calcium activity are improved. Applying our method to real calcium data, more regions exhibiting calcium activity in the full field of view were found. In road pavement crack detection, smaller or milder cracks were detected after using our enhancement method.
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Radojevic M, Meijering E. Automated neuron tracing using probability hypothesis density filtering. Bioinformatics 2017; 33:1073-1080. [PMID: 28065895 DOI: 10.1093/bioinformatics/btw751] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 11/22/2016] [Indexed: 01/18/2023] Open
Abstract
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.
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Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons. Neuroinformatics 2016; 14:201-19. [PMID: 26701809 PMCID: PMC4823367 DOI: 10.1007/s12021-015-9287-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
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.
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Improved detection of soma location and morphology in fluorescence microscopy images of neurons. J Neurosci Methods 2016; 274:61-70. [PMID: 27688018 DOI: 10.1016/j.jneumeth.2016.09.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 09/20/2016] [Accepted: 09/21/2016] [Indexed: 01/15/2023]
Abstract
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.
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Hieber SE, Bikis C, Khimchenko A, Schweighauser G, Hench J, Chicherova N, Schulz G, Müller B. Tomographic brain imaging with nucleolar detail and automatic cell counting. Sci Rep 2016; 6:32156. [PMID: 27581254 PMCID: PMC5007499 DOI: 10.1038/srep32156] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 07/19/2016] [Indexed: 01/27/2023] Open
Abstract
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.
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Affiliation(s)
- Simone E Hieber
- Biomaterials Science Center, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123 Allschwil, Switzerland
| | - Christos Bikis
- Biomaterials Science Center, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123 Allschwil, Switzerland
| | - Anna Khimchenko
- Biomaterials Science Center, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123 Allschwil, Switzerland
| | - Gabriel Schweighauser
- Institute of Pathology, Department of Neuropathology, University Hospital of Basel, Schönbeinstrasse 40, 4001 Basel, Switzerland
| | - Jürgen Hench
- Institute of Pathology, Department of Neuropathology, University Hospital of Basel, Schönbeinstrasse 40, 4001 Basel, Switzerland
| | - Natalia Chicherova
- Biomaterials Science Center, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123 Allschwil, Switzerland.,Medical Image Analysis Center, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123 Allschwil, Switzerland
| | - Georg Schulz
- Biomaterials Science Center, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123 Allschwil, Switzerland
| | - Bert Müller
- Biomaterials Science Center, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, 4123 Allschwil, Switzerland
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Detrez JR, Verstraelen P, Gebuis T, Verschuuren M, Kuijlaars J, Langlois X, Nuydens R, Timmermans JP, De Vos WH. Image Informatics Strategies for Deciphering Neuronal Network Connectivity. ADVANCES IN ANATOMY, EMBRYOLOGY, AND CELL BIOLOGY 2016; 219:123-48. [PMID: 27207365 DOI: 10.1007/978-3-319-28549-8_5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Amongst the neuronal structures that show morphological plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular communication and the associated calcium bursting behaviour. In vitro cultured neuronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardization of both image acquisition and image analysis, it has become possible to extract statistically relevant readouts from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies.
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Affiliation(s)
- Jan R Detrez
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Peter Verstraelen
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Titia Gebuis
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
| | - Marlies Verschuuren
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Jacobine Kuijlaars
- Neuroscience Department, Janssen Research and Development, Turnhoutseweg 30, 2340, Beerse, Belgium
- Laboratory for Cell Physiology, Biomedical Research Institute (BIOMED), Hasselt University, Agoralaan, 3590, Diepenbeek, Belgium
| | - Xavier Langlois
- Neuroscience Department, Janssen Research and Development, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Rony Nuydens
- Neuroscience Department, Janssen Research and Development, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Jean-Pierre Timmermans
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Winnok H De Vos
- Laboratory of Cell Biology and Histology, Department of Veterinary Sciences, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium.
- Cell Systems and Cellular Imaging, Department Molecular Biotechnology, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.
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Ozcan B, Negi P, Laezza F, Papadakis M, Labate D. Automated detection of soma location and morphology in neuronal network cultures. PLoS One 2015; 10:e0121886. [PMID: 25853656 PMCID: PMC4390318 DOI: 10.1371/journal.pone.0121886] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Accepted: 02/04/2015] [Indexed: 01/05/2023] Open
Abstract
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.
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Affiliation(s)
- Burcin Ozcan
- Dept. of Mathematics, University of Houston, Houston, Texas, United States of America
| | - Pooran Negi
- Dept. of Mathematics, University of Houston, Houston, Texas, United States of America
| | - Fernanda Laezza
- Dept. of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Manos Papadakis
- Dept. of Mathematics, University of Houston, Houston, Texas, United States of America
| | - Demetrio Labate
- Dept. of Mathematics, University of Houston, Houston, Texas, United States of America
- * E-mail:
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Megjhani M, Rey-Villamizar N, Merouane A, Lu Y, Mukherjee A, Trett K, Chong P, Harris C, Shain W, Roysam B. Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors. ACTA ACUST UNITED AC 2015; 31:2190-8. [PMID: 25701570 DOI: 10.1093/bioinformatics/btv109] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Accepted: 02/16/2015] [Indexed: 11/14/2022]
Abstract
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.
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Affiliation(s)
- Murad Megjhani
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Nicolas Rey-Villamizar
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Amine Merouane
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Yanbin Lu
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Amit Mukherjee
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Kristen Trett
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Peter Chong
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Carolyn Harris
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - William Shain
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Badrinath Roysam
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
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Sümbül U, Zlateski A, Vishwanathan A, Masland RH, Seung HS. Automated computation of arbor densities: a step toward identifying neuronal cell types. Front Neuroanat 2014; 8:139. [PMID: 25505389 PMCID: PMC4243570 DOI: 10.3389/fnana.2014.00139] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Accepted: 11/06/2014] [Indexed: 11/17/2022] Open
Abstract
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.
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Affiliation(s)
- Uygar Sümbül
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology Cambridge, MA, USA ; Department of Ophthalmology, Harvard Medical School Boston, MA, USA
| | - Aleksandar Zlateski
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology Cambridge, MA, USA
| | - Ashwin Vishwanathan
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology Cambridge, MA, USA
| | - Richard H Masland
- Department of Ophthalmology, Harvard Medical School Boston, MA, USA ; Department of Neurobiology, Harvard Medical School Boston, MA, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute and Computer Science Department, Princeton University Princeton, NJ, USA
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15
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Gala R, Chapeton J, Jitesh J, Bhavsar C, Stepanyants A. Active learning of neuron morphology for accurate automated tracing of neurites. Front Neuroanat 2014; 8:37. [PMID: 24904306 PMCID: PMC4032887 DOI: 10.3389/fnana.2014.00037] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Accepted: 04/30/2014] [Indexed: 11/24/2022] Open
Abstract
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.
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Affiliation(s)
- Rohan Gala
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA
| | - Julio Chapeton
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA
| | - Jayant Jitesh
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA
| | - Chintan Bhavsar
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA
| | - Armen Stepanyants
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA
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16
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Ulrich H, Bocsi J, Glaser T, Tárnok A. Cytometry in the brain: studying differentiation to diagnostic applications in brain disease and regeneration therapy. Cell Prolif 2014; 47:12-9. [PMID: 24450810 DOI: 10.1111/cpr.12087] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2013] [Accepted: 11/02/2013] [Indexed: 12/30/2022] Open
Abstract
During brain development, a population of uniform embryonic cells migrates and differentiates into a large number of neural phenotypes - origin of the enormous complexity of the adult nervous system. Processes of cell proliferation, differentiation and programmed death of no longer required cells, do not occur only during embryogenesis, but are also maintained during adulthood and are affected in neurodegenerative and neuropsychiatric disease states. As neurogenesis is an endogenous response to brain injury, visible as proliferation (of to this moment silent stem or progenitor cells), its further stimulation can present a treatment strategy in addition to stem cell transfer for cell regeneration therapy. Concise techniques for studying such events in vitro and in vivo permit understanding of underlying mechanisms. Detection of subtle physiological alterations in brain cell proliferation and neurogenesis can be explored, that occur during environmental stimulation, exercise and ageing. Here, we have collected achievements in the field of basic research on applications of cytometry, including automated imaging for quantification of morphological or fluorescence-based parameters in cell cultures, towards imaging of three-dimensional brain architecture together with DNA content and proliferation data. Multi-parameter and more recently in vivo flow cytometry procedures, have been developed for quantification of phenotypic diversity and cell processes that occur during brain development as well as in adulthood, with importance for therapeutic approaches.
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Affiliation(s)
- H Ulrich
- Department of Biochemistry, Institute of Chemistry, University of Sao Paulo, São Paulo, S.P 05508-900, Brazil
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17
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Ming X, Li A, Wu J, Yan C, Ding W, Gong H, Zeng S, Liu Q. Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling. PLoS One 2013; 8:e84557. [PMID: 24391966 PMCID: PMC3877282 DOI: 10.1371/journal.pone.0084557] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 11/16/2013] [Indexed: 11/22/2022] Open
Abstract
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.
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Affiliation(s)
- Xing Ming
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Jingpeng Wu
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Cheng Yan
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Wenxiang Ding
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Liu
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan, China
- MoE Key Laboratory of Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
- * E-mail:
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18
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Counter WB, Wang IQ, Farncombe TH, Labiris NR. Airway and pulmonary vascular measurements using contrast-enhanced micro-CT in rodents. Am J Physiol Lung Cell Mol Physiol 2013; 304:L831-43. [DOI: 10.1152/ajplung.00281.2012] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Preclinical imaging allows pulmonary researchers to study lung disease and pulmonary drug delivery noninvasively and longitudinally in small animals. However, anatomically localizing a pathology or drug deposition to a particular lung region is not easily done. Thus, a detailed knowledge of the anatomical structure of small animal lungs is necessary for understanding disease progression and in addition would facilitate the analysis of the imaging data, mapping drug deposition and relating function to structure. In this study, contrast-enhanced micro-computed tomography (CT) of the lung produced high-resolution images that allowed for the characterization of the rodent airway and pulmonary vasculature. Contrast-enhanced micro-CT was used to visualize the airways and pulmonary vasculature in Sprague-Dawley rats (200–225 g) and BALB/c mice (20–25 g) postmortem. Segmented volumes from these images were processed to yield automated measurements of the airways and pulmonary vasculature. The diameters, lengths, and branching angles of the airway, arterial, and venous trees were measured and analyzed as a function of generation number and vessel diameter to establish rules that could be applied at all levels of tree hierarchy. In the rat, airway, arterial, and venous tress were measured down to the 20th, 16th, and 14th generation, respectively. In the mouse, airway, arterial, and venous trees were measured down to the 16th, 8th, and 7th generation, respectively. This structural information, catalogued in a rodent database, will increase our understanding of lung structure and will aid in future studies of the relationship between structure and function in animal models of disease.
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Affiliation(s)
- W. B. Counter
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Department of Medical Physics, McMaster University, Hamilton, Ontario, Canada
| | - I. Q. Wang
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - T. H. Farncombe
- Department of Radiology, McMaster University, Hamilton, Ontario, Canada; and
- Department of Nuclear Medicine, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - N. R. Labiris
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Department of Nuclear Medicine, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
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19
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Automated and accurate detection of soma location and surface morphology in large-scale 3D neuron images. PLoS One 2013; 8:e62579. [PMID: 23638117 PMCID: PMC3634810 DOI: 10.1371/journal.pone.0062579] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2012] [Accepted: 03/21/2013] [Indexed: 11/19/2022] Open
Abstract
Automated and accurate localization and morphometry of somas in 3D neuron images is essential for quantitative studies of neural networks in the brain. However, previous methods are limited in obtaining the location and surface morphology of somas with variable size and uneven staining in large-scale 3D neuron images. In this work, we proposed a method for automated soma locating in large-scale 3D neuron images that contain relatively sparse soma distributions. This method involves three steps: (i) deblocking the image with overlap between adjacent sub-stacks; (ii) locating the somas in each small sub-stack using multi-scale morphological close and adaptive thresholds; and (iii) fusion of the repeatedly located somas in all sub-stacks. We also describe a new method for the accurate detection of the surface morphology of somas containing hollowness; this was achieved by improving the classical Rayburst Sampling with a new gradient-based criteria. Three 3D neuron image stacks of different sizes were used to quantitatively validate our methods. For the soma localization algorithm, the average recall and precision were greater than 93% and 96%, respectively. For the soma surface detection algorithm, the overlap of the volumes created by automatic detection of soma surfaces and manually segmenting soma volumes was more than 84% for 89% of all correctly detected somas. Our method for locating somas can reveal the soma distributions in large-scale neural networks more efficiently. The method for soma surface detection will serve as a valuable tool for systematic studies of neuron types based on neuron structure.
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20
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Choromanska A, Chang SF, Yuste R. Automatic reconstruction of neural morphologies with multi-scale tracking. Front Neural Circuits 2012; 6:25. [PMID: 22754498 PMCID: PMC3385559 DOI: 10.3389/fncir.2012.00025] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2011] [Accepted: 04/19/2012] [Indexed: 11/21/2022] Open
Abstract
Neurons have complex axonal and dendritic morphologies that are the structural building blocks of neural circuits. The traditional method to capture these morphological structures using manual reconstructions is time-consuming and partly subjective, so it appears important to develop automatic or semi-automatic methods to reconstruct neurons. Here we introduce a fast algorithm for tracking neural morphologies in 3D with simultaneous detection of branching processes. The method is based on existing tracking procedures, adding the machine vision technique of multi-scaling. Starting from a seed point, our algorithm tracks axonal or dendritic arbors within a sphere of a variable radius, then moves the sphere center to the point on its surface with the shortest Dijkstra path, detects branching points on the surface of the sphere, scales it until branches are well separated and then continues tracking each branch. We evaluate the performance of our algorithm on preprocessed data stacks obtained by manual reconstructions of neural cells, corrupted with different levels of artificial noise, and unprocessed data sets, achieving 90% precision and 81% recall in branch detection. We also discuss limitations of our method, such as reconstructing highly overlapping neural processes, and suggest possible improvements. Multi-scaling techniques, well suited to detect branching structures, appear a promising strategy for automatic neuronal reconstructions.
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Affiliation(s)
- Anna Choromanska
- Department of Electrical Engineering, Columbia University New York, NY, USA
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21
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Zhao T, Xie J, Amat F, Clack N, Ahammad P, Peng H, Long F, Myers E. Automated reconstruction of neuronal morphology based on local geometrical and global structural models. Neuroinformatics 2012; 9:247-61. [PMID: 21547564 PMCID: PMC3104133 DOI: 10.1007/s12021-011-9120-3] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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.
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Affiliation(s)
- Ting Zhao
- Qiushi Academy for Advanced Studies, Zhejiang University, 38 ZheDa Road, Hangzhou, 310027 China
| | - Jun Xie
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Fernando Amat
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Nathan Clack
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Parvez Ahammad
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Hanchuan Peng
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Fuhui Long
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Eugene Myers
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
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22
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Tsechpenakis G, Mukherjee P, Kim MD, Chiba A. Three-dimensional motor neuron morphology estimation in the Drosophila ventral nerve cord. IEEE Trans Biomed Eng 2011; 59:1253-63. [PMID: 22203698 DOI: 10.1109/tbme.2011.2181166] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Type-specific dendritic arborization patterns dictate synaptic connectivity and are fundamental determinants of neuronal function. We exploit the morphological stereotypy and relative simplicity of the Drosophila nervous system to model the diverse neuronal morphologies of individual motor neurons (MNs) and understand underlying principles of synaptic connectivity in a motor circuit. Our computational approach aims at the reconstruction of the neuron morphology, namely the robust segmentation of the neuron volumes from their surroundings with the simultaneous partitioning into their compartments, namely the soma, axon, and dendrites. We use the idea of cosegmentation, where every image along the z -axis (depth) is segmented using information from "neighboring" depths. We use 3-D Haar-like features to model appearance. Because soma and axon are determined by their distinctive shapes, we define an implicit shape representation of the 2-D segmentation sets to drive cosegmentation and achieve the desired partitioning. We validate our method using image stacks depicting single neurons labeled with green fluorescent protein (GFP) and serially imaged with laser scanning confocal microscopy.
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Affiliation(s)
- Gavriil Tsechpenakis
- Computer and Information Science Department, Indiana University-Purdue University, Indianapolis, IN 46202, USA.
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23
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Xie J, Zhao T, Lee T, Myers E, Peng H. Anisotropic path searching for automatic neuron reconstruction. Med Image Anal 2011; 15:680-9. [PMID: 21669547 DOI: 10.1016/j.media.2011.05.013] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2011] [Revised: 05/16/2011] [Accepted: 05/18/2011] [Indexed: 11/24/2022]
Abstract
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.
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Affiliation(s)
- Jun Xie
- Janelia Farm Research Campus, Howard Hughes Medical Institute, 19700 Helix Drive, Ashburn, VA 20147, USA.
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24
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Automated reconstruction of neuronal morphology: an overview. ACTA ACUST UNITED AC 2010; 67:94-102. [PMID: 21118703 DOI: 10.1016/j.brainresrev.2010.11.003] [Citation(s) in RCA: 114] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2010] [Revised: 11/13/2010] [Accepted: 11/16/2010] [Indexed: 12/14/2022]
Abstract
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.
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25
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Abstract
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.
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Affiliation(s)
- Erik Meijering
- Biomedical Imaging Group Rotterdam, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
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26
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Forero MG, Pennack JA, Hidalgo A. DeadEasy neurons: automatic counting of HB9 neuronal nuclei in Drosophila. Cytometry A 2010; 77:371-8. [PMID: 20162534 DOI: 10.1002/cyto.a.20877] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Research into the genetic basis of nervous system development and neurodegenerative diseases requires counting neurons to find out the extent of neurogenesis or neuronal loss. Drosophila is a widely used model organism for in vivo studies. However, counting neurons throughout the nervous system of the intact animal is humanly unfeasible. Automatic methods for cell counting in intact Drosophila are desirable. Here, we show a method called DeadEasy Neurons to count the number of neurons stained with anti-HB9 antibodies in Drosophila embryos. DeadEasy Neurons employs image filtering and mathematical morphology techniques in 2D and 3D, followed by identification of nuclei in 3D based on minimum volume, to count automatically the number of HB9 neurons in vivo. The resultant method has been validated for Drosophila embryos and we show here how it can be used to address biological questions. Counting neurons with DeadEasy is very fast, extremely accurate, and objective, and it enables analyses otherwise humanly unmanageable. DeadEasy Neurons can be modified by the user for other applications, and it will be freely available as an ImageJ plug-in. DeadEasy Neurons will be of interest to the microscopy, image processing, Drosophila, neurobiology, and biomedical communities.
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Affiliation(s)
- Manuel G Forero
- NeuroDevelopment Group, University of Birmingham, Birmingham, United Kingdom
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27
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Huang Y, Zhou X, Miao B, Lipinski M, Zhang Y, Li F, Degterev A, Yuan J, Hu G, Wong STC. A computational framework for studying neuron morphology from in vitro high content neuron-based screening. J Neurosci Methods 2010; 190:299-309. [PMID: 20580743 DOI: 10.1016/j.jneumeth.2010.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2009] [Revised: 05/11/2010] [Accepted: 05/16/2010] [Indexed: 10/19/2022]
Abstract
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.
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Affiliation(s)
- Yue Huang
- Methodist Hospital Research Institute, Radiology Department, Houston, TX 77030, USA
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28
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Pawliczek P, Romanowska-Pawliczek A, Soltys Z. Parallel deconvolution of large 3D images obtained by confocal laser scanning microscopy. Microsc Res Tech 2010; 73:187-94. [PMID: 19725070 DOI: 10.1002/jemt.20773] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Various deconvolution algorithms are often used for restoration of digital images. Image deconvolution is especially needed for the correction of three-dimensional images obtained by confocal laser scanning microscopy. Such images suffer from distortions, particularly in the Z dimension. As a result, reliable automatic segmentation of these images may be difficult or even impossible. Effective deconvolution algorithms are memory-intensive and time-consuming. In this work, we propose a parallel version of the well-known Richardson-Lucy deconvolution algorithm developed for a system with distributed memory and implemented with the use of Message Passing Interface (MPI). It enables significantly more rapid deconvolution of two-dimensional and three-dimensional images by efficiently splitting the computation across multiple computers. The implementation of this algorithm can be used on professional clusters provided by computing centers as well as on simple networks of ordinary PC machines.
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Affiliation(s)
- Piotr Pawliczek
- Department of Computer Science, Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, AGH University of Science and Technology, Kraków.
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29
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Yuan X, Trachtenberg JT, Potter SM, Roysam B. MDL constrained 3-D grayscale skeletonization algorithm for automated extraction of dendrites and spines from fluorescence confocal images. Neuroinformatics 2009; 7:213-32. [PMID: 20012509 DOI: 10.1007/s12021-009-9057-y] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2008] [Accepted: 10/30/2009] [Indexed: 11/27/2022]
Abstract
This paper presents a method for improved automatic delineation of dendrites and spines from three-dimensional (3-D) images of neurons acquired by confocal or multi-photon fluorescence microscopy. The core advance presented here is a direct grayscale skeletonization algorithm that is constrained by a structural complexity penalty using the minimum description length (MDL) principle, and additional neuroanatomy-specific constraints. The 3-D skeleton is extracted directly from the grayscale image data, avoiding errors introduced by image binarization. The MDL method achieves a practical tradeoff between the complexity of the skeleton and its coverage of the fluorescence signal. Additional advances include the use of 3-D spline smoothing of dendrites to improve spine detection, and graph-theoretic algorithms to explore and extract the dendritic structure from the grayscale skeleton using an intensity-weighted minimum spanning tree (IW-MST) algorithm. This algorithm was evaluated on 30 datasets organized in 8 groups from multiple laboratories. Spines were detected with false negative rates less than 10% on most datasets (the average is 7.1%), and the average false positive rate was 11.8%. The software is available in open source form.
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Affiliation(s)
- Xiaosong Yuan
- Jonsson Engineering Center, Center for Subsurface Sensing & Imaging Systems, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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Rodriguez A, Ehlenberger DB, Hof PR, Wearne SL. Three-dimensional neuron tracing by voxel scooping. J Neurosci Methods 2009; 184:169-75. [PMID: 19632273 DOI: 10.1016/j.jneumeth.2009.07.021] [Citation(s) in RCA: 62] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2009] [Revised: 07/16/2009] [Accepted: 07/16/2009] [Indexed: 11/30/2022]
Abstract
Tracing the centerline of the dendritic arbor of neurons is a powerful technique for analyzing neuronal morphology. In the various neuron tracing algorithms in use nowadays, the competing goals of computational efficiency and robustness are generally traded off against each other. We present a novel method for tracing the centerline of a neuron from confocal image stacks, which provides an optimal balance between these objectives. Using only local information, thin cross-sectional layers of voxels ('scoops') are iteratively carved out of the structure, and clustered based on connectivity. Each cluster contributes a node along the centerline, which is created by connecting successive nodes until all object voxels are exhausted. While data segmentation is independent of this algorithm, we illustrate the use of the ISODATA method to achieve dynamic (local) segmentation. Diameter estimation at each node is calculated using the Rayburst Sampling algorithm, and spurious end nodes caused by surface irregularities are then removed. On standard computing hardware the algorithm can process hundreds of thousands of voxels per second, easily handling the multi-gigabyte datasets resulting from high-resolution confocal microscopy imaging of neurons. This method provides an accurate and efficient means for centerline extraction that is suitable for interactive neuron tracing applications.
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Affiliation(s)
- Alfredo Rodriguez
- Department of Neuroscience, Mount Sinai School of Medicine, New York, NY, USA.
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Yu W, Lee HK, Hariharan S, Bu W, Ahmed S. Quantitative neurite outgrowth measurement based on image segmentation with topological dependence. Cytometry A 2009; 75:289-97. [PMID: 18951464 DOI: 10.1002/cyto.a.20664] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The study of neuronal morphology and neurite outgrowth has been enhanced by the combination of imaging informatics and high content screening, in which thousands of images are acquired using robotic fluorescent microscopy. To understand the process of neurite outgrowth in the context of neuroregeneration, we used mouse neuroblastoma N1E115 as our model neuronal cell. Six-thousand cellular images of four different culture conditions were acquired with two-channel widefield fluorescent microscopy. We developed a software package called NeuronCyto. It is a fully automatic solution for neurite length measurement and complexity analysis. A novel approach based on topological analysis is presented to segment cells. The detected nuclei were used as references to initialize the level set function. Merging and splitting of cells segments were prevented using dynamic watershed lines based on the constraint of topological dependence. A tracing algorithm was developed to automatically trace neurites and measure their lengths quantitatively on a cell-by-cell basis. NeuronCyto analyzes three important biologically relevant features, which are the length, branching complexity, and number of neurites. The application of NeuronCyto on the experiments of Toca-1 and serum starvation show that the transfection of Toca-1 cDNA induces longer neurites with more complexities than serum starvation.
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Affiliation(s)
- Weimiao Yu
- Imaging Informatics Division, Bioinformatics Institute (BII), Matrix, Singapore.
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Kwon HS, Nam YS, Wiktor-Brown DM, Engelward BP, So PTC. Quantitative morphometric measurements using site selective image cytometry of intact tissue. J R Soc Interface 2009; 6 Suppl 1:S45-57. [PMID: 19049958 DOI: 10.1098/rsif.2008.0431.focus] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Site selective two-photon tissue image cytometry has previously been successfully applied to measure the number of rare cells in three-dimensional tissue specimens up to cubic millimetres in size. However, the extension of this approach for high-throughput quantification of cellular morphological states has not been demonstrated. In this paper, we report the use of site-selective tissue image cytometry for the study of homologous recombination (HR) events during cell division in the pancreas of transgenic mice. Since HRs are rare events, recombinant cells distribute sparsely inside the organ. A detailed measurement throughout the whole tissue is thus not practical. Instead, the site selective two-photon tissue cytometer incorporates a low magnification, wide field, one-photon imaging subsystem that rapidly identifies regions of interest containing recombinant cell clusters. Subsequently, high-resolution three-dimensional assays based on two-photon microscopy can be performed only in these regions of interest. We further show that three-dimensional morphology extraction algorithms can be used to analyse the resultant high-resolution two-photon image stacks providing information not only on the frequency and the distribution of these recombinant cell clusters and their constituent cells, but also on their morphology.
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Affiliation(s)
- Hyuk-Sang Kwon
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Jun SB, Hynd MR, Dowell-Mesfin NM, Al-Kofahi Y, Roysam B, Shain W, Kim SJ. Modulation of cultured neural networks using neurotrophin release from hydrogel-coated microelectrode arrays. J Neural Eng 2008; 5:203-13. [PMID: 18477815 DOI: 10.1088/1741-2560/5/2/011] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Polyacrylamide and poly(ethylene glycol) diacrylate hydrogels were synthesized and characterized for use as drug release and substrates for neuron cell culture. Protein release kinetics was determined by incorporating bovine serum albumin (BSA) into hydrogels during polymerization. To determine if hydrogel incorporation and release affect bioactivity, alkaline phosphatase was incorporated into hydrogels and a released enzyme activity determined using the fluorescence-based ELF-97 assay. Hydrogels were then used to deliver a brain-derived neurotrophic factor (BDNF) from hydrogels polymerized over planar microelectrode arrays (MEAs). Primary hippocampal neurons were cultured on both control and neurotrophin-containing hydrogel-coated MEAs. The effect of released BDNF on neurite length and process arborization was investigated using automated image analysis. An increased spontaneous activity as a response to the released BDNF was recorded from the neurons cultured on the top of hydrogel layers. These results demonstrate that proteins of biological interest can be incorporated into hydrogels to modulate development and function of cultured neural networks. These results also set the stage for development of hydrogel-coated neural prosthetic devices for local delivery of various biologically active molecules.
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Affiliation(s)
- Sang Beom Jun
- Nano-Bioelectronics and Systems Research Center, Seoul, Korea
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Bjornsson CS, Lin G, Al-Kofahi Y, Narayanaswamy A, Smith KL, Shain W, Roysam B. Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue. J Neurosci Methods 2008; 170:165-78. [PMID: 18294697 DOI: 10.1016/j.jneumeth.2007.12.024] [Citation(s) in RCA: 74] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2007] [Revised: 12/06/2007] [Accepted: 12/27/2007] [Indexed: 10/22/2022]
Abstract
Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic 'divide and conquer' methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick ( approximately 100 microm) slices of rat brain tissue were labeled using three to five fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81 to 92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system.
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Affiliation(s)
- Christopher S Bjornsson
- Center for Neural Communication Technology, New York State Department of Health, Wadsworth Center, Albany, NY 12201-0509, USA
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