1
|
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.
Collapse
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
| |
Collapse
|
2
|
Abstract
Digital reconstruction of a single neuron occupies an important position in computational neuroscience. Although many novel methods have been proposed, recent advances in molecular labeling and imaging systems allow for the production of large and complicated neuronal datasets, which pose many challenges for neuron reconstruction, especially when discontinuous neuronal morphology appears in a strong noise environment. Here, we develop a new pipeline to address this challenge. Our pipeline is based on two methods, one is the region-to-region connection (RRC) method for detecting the initial part of a neurite, which can effectively gather local cues, i.e., avoid the whole image analysis, and thus boosts the efficacy of computation; the other is constrained principal curves method for completing the neurite reconstruction, which uses the past reconstruction information of a neurite for current reconstruction and thus can be suitable for tracing discontinuous neurites. We investigate the reconstruction performances of our pipeline and some of the best state-of-the-art algorithms on the experimental datasets, indicating the superiority of our method in reconstructing sparsely distributed neurons with discontinuous neuronal morphologies in noisy environment. We show the strong ability of our pipeline in dealing with the large-scale image dataset. We validate the effectiveness in dealing with various kinds of image stacks including those from the DIADEM challenge and BigNeuron project.
Collapse
|
3
|
Santamaría-Pang A, Hernandez-Herrera P, Papadakis M, Saggau P, Kakadiaris IA. Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models. Neuroinformatics 2016; 13:297-320. [PMID: 25631538 DOI: 10.1007/s12021-014-9253-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The challenges faced in analyzing optical imaging data from neurons include a low signal-to-noise ratio of the acquired images and the multiscale nature of the tubular structures that range in size from hundreds of microns to hundreds of nanometers. In this paper, we address these challenges and present a computational framework for an automatic, three-dimensional (3D) morphological reconstruction of live nerve cells. The key aspects of this approach are: (i) detection of neuronal dendrites through learning 3D tubular models, and (ii) skeletonization by a new algorithm using a morphology-guided deformable model for extracting the dendritic centerline. To represent the neuron morphology, we introduce a novel representation, the Minimum Shape-Cost (MSC) Tree that approximates the dendrite centerline with sub-voxel accuracy and demonstrate the uniqueness of such a shape representation as well as its computational efficiency. We present extensive quantitative and qualitative results that demonstrate the accuracy and robustness of our method.
Collapse
Affiliation(s)
- Alberto Santamaría-Pang
- Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX, 77204, USA
| | | | | | | | | |
Collapse
|
4
|
Luo G, Sui D, Wang K, Chae J. Neuron anatomy structure reconstruction based on a sliding filter. BMC Bioinformatics 2015; 16:342. [PMID: 26498293 PMCID: PMC4619512 DOI: 10.1186/s12859-015-0780-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 10/16/2015] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Reconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task. METHODS This paper introduces a new pipeline for reconstructing neuron anatomy structure from 3-D microscopy image stacks. This pipeline is initialized with a set of seeds that were detected by our proposed Sliding Volume Filter (SVF), given a non-circular cross-section of a neuron cell. Then, an improved open curve snake model combined with a SVF external force is applied to trace the full skeleton of the neuron cell. A radius estimation method based on a 2D sliding band filter is developed to fit the real edge of the cross-section of the neuron cell. Finally, a surface reconstruction method based on non-parallel curve networks is used to generate the neuron cell surface to finish this pipeline. RESULTS The proposed pipeline has been evaluated using publicly available datasets. The results show that the proposed method achieves promising results in some datasets from the DIgital reconstruction of Axonal and DEndritic Morphology (DIADEM) challenge and new BigNeuron project. CONCLUSION The new pipeline works well in neuron tracing and reconstruction. It can achieve higher efficiency, stability and robustness in neuron skeleton tracing. Furthermore, the proposed radius estimation method and applied surface reconstruction method can obtain more accurate neuron anatomy structures.
Collapse
Affiliation(s)
- Gongning Luo
- Research Center of Perception and Computing, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
| | - Dong Sui
- Research Center of Perception and Computing, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
| | - Kuanquan Wang
- Research Center of Perception and Computing, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
| | - Jinseok Chae
- Department of Computer Science and Engineering, Incheon National University, Incheon, Korea.
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
A pipeline for neuron reconstruction based on spatial sliding volume filter seeding. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014; 2014:386974. [PMID: 25101141 PMCID: PMC4101938 DOI: 10.1155/2014/386974] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2014] [Accepted: 06/16/2014] [Indexed: 11/17/2022]
Abstract
Neuron's shape and dendritic architecture are important for biosignal transduction in neuron networks. And the anatomy architecture reconstruction of neuron cell is one of the foremost challenges and important issues in neuroscience. Accurate reconstruction results can facilitate the subsequent neuron system simulation. With the development of confocal microscopy technology, researchers can scan neurons at submicron resolution for experiments. These make the reconstruction of complex dendritic trees become more feasible; however, it is still a tedious, time consuming, and labor intensity task. For decades, computer aided methods have been playing an important role in this task, but none of the prevalent algorithms can reconstruct full anatomy structure automatically. All of these make it essential for developing new method for reconstruction. This paper proposes a pipeline with a novel seeding method for reconstructing neuron structures from 3D microscopy images stacks. The pipeline is initialized with a set of seeds detected by sliding volume filter (SVF), and then the open curve snake is applied to the detected seeds for reconstructing the full structure of neuron cells. The experimental results demonstrate that the proposed pipeline exhibits excellent performance in terms of accuracy compared with traditional method, which is clearly a benefit for 3D neuron detection and reconstruction.
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Hirst TC, Ribchester RR. Segmentation of the mouse fourth deep lumbrical muscle connectome reveals concentric organisation of motor units. J Physiol 2013; 591:4859-75. [PMID: 23940381 DOI: 10.1113/jphysiol.2013.258087] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Connectomic analysis of the nervous system aims to discover and establish principles that underpin normal and abnormal neural connectivity and function. Here we performed image analysis of motor unit connectivity in the fourth deep lumbrical muscle (4DL) of mice, using transgenic expression of fluorescent protein in motor neurones as a morphological reporter. We developed a method that accelerated segmentation of confocal image projections of 4DL motor units, by applying high resolution (63×, 1.4 NA objective) imaging or deconvolution only where either proved necessary, in order to resolve axon crossings that produced ambiguities in the correct assignment of axon terminals to identified motor units imaged at lower optical resolution (40×, 1.3 NA). The 4DL muscles contained between 4 and 9 motor units and motor unit sizes ranged in distribution from 3 to 111 motor nerve terminals per unit. Several structural properties of the motor units were consistent with those reported in other muscles, including suboptimal wiring length and distribution of motor unit size. Surprisingly, however, small motor units were confined to a region of the muscle near the nerve entry point, whereas their larger counterparts were progressively more widely dispersed, suggesting a previously unrecognised form of segregated motor innervation in this muscle. We also found small but significant differences in variance of motor endplate length in motor units, which correlated weakly with their motor unit size. Thus, our connectomic analysis has revealed a pattern of concentric innervation that may perhaps also exist in other, cylindrical muscles that have not previously been thought to show segregated motor unit organisation. This organisation may be the outcome of competition during postnatal development based on intrinsic neuronal differences in synaptic size or synaptic strength that generates a territorial hierarchy in motor unit size and disposition.
Collapse
Affiliation(s)
- Theodore C Hirst
- R. R. Ribchester: Euan Macdonald Centre for Motor Neurone Disease Research, Centre for Integrative Physiology, The University of Edinburgh, Edinburgh EH8 9XD, UK.
| | | |
Collapse
|
9
|
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.
Collapse
Affiliation(s)
- Anna Choromanska
- Department of Electrical Engineering, Columbia University New York, NY, USA
| | | | | |
Collapse
|
10
|
Graph theoretical model of a sensorimotor connectome in zebrafish. PLoS One 2012; 7:e37292. [PMID: 22624008 PMCID: PMC3356276 DOI: 10.1371/journal.pone.0037292] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2012] [Accepted: 04/19/2012] [Indexed: 01/20/2023] Open
Abstract
Mapping the detailed connectivity patterns (connectomes) of neural circuits is a central goal of neuroscience. The best quantitative approach to analyzing connectome data is still unclear but graph theory has been used with success. We present a graph theoretical model of the posterior lateral line sensorimotor pathway in zebrafish. The model includes 2,616 neurons and 167,114 synaptic connections. Model neurons represent known cell types in zebrafish larvae, and connections were set stochastically following rules based on biological literature. Thus, our model is a uniquely detailed computational representation of a vertebrate connectome. The connectome has low overall connection density, with 2.45% of all possible connections, a value within the physiological range. We used graph theoretical tools to compare the zebrafish connectome graph to small-world, random and structured random graphs of the same size. For each type of graph, 100 randomly generated instantiations were considered. Degree distribution (the number of connections per neuron) varied more in the zebrafish graph than in same size graphs with less biological detail. There was high local clustering and a short average path length between nodes, implying a small-world structure similar to other neural connectomes and complex networks. The graph was found not to be scale-free, in agreement with some other neural connectomes. An experimental lesion was performed that targeted three model brain neurons, including the Mauthner neuron, known to control fast escape turns. The lesion decreased the number of short paths between sensory and motor neurons analogous to the behavioral effects of the same lesion in zebrafish. This model is expandable and can be used to organize and interpret a growing database of information on the zebrafish connectome.
Collapse
|
11
|
Abstract
This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.
Collapse
|
12
|
|