1
|
Liu Y, Zhong Y, Zhao X, Liu L, Ding L, Peng H. Tracing weak neuron fibers. Bioinformatics 2022; 39:6960919. [PMID: 36571479 PMCID: PMC9848051 DOI: 10.1093/bioinformatics/btac816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 11/01/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons. RESULTS We proposed a method, named the NeuMiner, for tracing weak fibers by combining two strategies: an online sample mining strategy and a modified gamma transformation. NeuMiner improved the recall of weak signals (voxel values <20) by a large margin, from 5.1 to 27.8%. This is prominent for axons, which increased by 6.4 times, compared to 2.0 times for dendrites. Both strategies were shown to be beneficial for weak fiber recognition, and they reduced the average axonal spatial distances to gold standards by 46 and 13%, respectively. The improvement was observed on two prevalent automatic tracing algorithms and can be applied to any other tracers and image types. AVAILABILITY AND IMPLEMENTATION Source codes of NeuMiner are freely available on GitHub (https://github.com/crazylyf/neuronet/tree/semantic_fnm). Image visualization, preprocessing and tracing are conducted on the Vaa3D platform, which is accessible at the Vaa3D GitHub repository (https://github.com/Vaa3D). All training and testing images are cropped from high-resolution fMOST mouse brains downloaded from the Brain Image Library (https://www.brainimagelibrary.org/), and the corresponding gold standards are available at https://doi.brainimagelibrary.org/doi/10.35077/g.25. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Yufeng Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | - Ye Zhong
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | - Xuan Zhao
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | - Lijuan Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | - Liya Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | | |
Collapse
|
2
|
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
|
3
|
Wang X, Liu M, Wang Y, Fan J, Meijering E. A 3D Tubular Flux Model for Centerline Extraction in Neuron Volumetric Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1069-1079. [PMID: 34826295 DOI: 10.1109/tmi.2021.3130987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Digital morphology reconstruction from neuron volumetric images is essential for computational neuroscience. The centerline of the axonal and dendritic tree provides an effective shape representation and serves as a basis for further neuron reconstruction. However, it is still a challenge to directly extract the accurate centerline from the complex neuron structure with poor image quality. In this paper, we propose a neuron centerline extraction method based on a 3D tubular flux model via a two-stage CNN framework. In the first stage, a 3D CNN is used to learn the latent neuron structure features, namely flux features, from neuron images. In the second stage, a light-weight U-Net takes the learned flux features as input to extract the centerline with a spatial weighted average strategy to constrain the multi-voxel width response. Specifically, the labels of flux features in the first stage are generated by the 3D tubular model which calculates the geometric representations of the flux between each voxel in the tubular region and the nearest point on the centerline ground truth. Compared with self-learned features by networks, flux features, as a kind of prior knowledge, explicitly take advantage of the contextual distance and direction distribution information around the centerline, which is beneficial for the precise centerline extraction. Experiments on two challenging datasets demonstrate that the proposed method outperforms other state-of-the-art methods by 18% and 35.1% in F1-measurement and average distance scores at the most, and the extracted centerline is helpful to improve the neuron reconstruction performance.
Collapse
|
4
|
Huang Q, Cao T, Zeng S, Li A, Quan T. Minimizing probability graph connectivity cost for discontinuous filamentary structures tracing in neuron image. IEEE J Biomed Health Inform 2022; 26:3092-3103. [PMID: 35104232 DOI: 10.1109/jbhi.2022.3147512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Neuron tracing from optical image is critical in understanding brain function in diseases. A key problem is to trace discontinuous filamentary structures from noisy background, which is commonly encountered in neuronal and some medical images. Broken traces lead to cumulative topological errors, and current methods were hard to assemble various fragmentary traces for correct connection. In this paper, we propose a graph connectivity theoretical method for precise filamentary structure tracing in neuron image. First, we build the initial subgraphs of signals via a region-to-region based tracing method on CNN predicted probability. CNN technique removes noise interference, whereas its prediction for some elongated fragments is still incomplete. Second, we reformulate the global connection problem of individual or fragmented subgraphs under heuristic graph restrictions as a dynamic linear programming function via minimizing graph connectivity cost, where the connected cost of breakpoints are calculated using their probability strength via minimum cost path. Experimental results on challenging neuronal images proved that the proposed method outperformed existing methods and achieved similar results of manual tracing, even in some complex discontinuous issues. Performances on vessel images indicate the potential of the method for some other tubular objects tracing.
Collapse
|
5
|
Huang Q, Cao T, Chen Y, Li A, Zeng S, Quan T. Automated Neuron Tracing Using Content-Aware Adaptive Voxel Scooping on CNN Predicted Probability Map. Front Neuroanat 2021; 15:712842. [PMID: 34497493 PMCID: PMC8419427 DOI: 10.3389/fnana.2021.712842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/29/2021] [Indexed: 11/23/2022] Open
Abstract
Neuron tracing, as the essential step for neural circuit building and brain information flow analyzing, plays an important role in the understanding of brain organization and function. Though lots of methods have been proposed, automatic and accurate neuron tracing from optical images remains challenging. Current methods often had trouble in tracing the complex tree-like distorted structures and broken parts of neurite from a noisy background. To address these issues, we propose a method for accurate neuron tracing using content-aware adaptive voxel scooping on a convolutional neural network (CNN) predicted probability map. First, a 3D residual CNN was applied as preprocessing to predict the object probability and suppress high noise. Then, instead of tracing on the binary image produced by maximum classification, an adaptive voxel scooping method was presented for successive neurite tracing on the probability map, based on the internal content properties (distance, connectivity, and probability continuity along direction) of the neurite. Last, the neuron tree graph was built using the length first criterion. The proposed method was evaluated on the public BigNeuron datasets and fluorescence micro-optical sectioning tomography (fMOST) datasets and outperformed current state-of-art methods on images with neurites that had broken parts and complex structures. The high accuracy tracing proved the potential of the proposed method for neuron tracing on large-scale.
Collapse
Affiliation(s)
- Qing Huang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Cao
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yijun Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
6
|
He Y, Huang J, Wu G, Yang J. Exploring highly reliable substructures in auto-reconstructions of a neuron. Brain Inform 2021; 8:17. [PMID: 34431008 PMCID: PMC8384950 DOI: 10.1186/s40708-021-00137-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 07/27/2021] [Indexed: 11/10/2022] Open
Abstract
The digital reconstruction of a neuron is the most direct and effective way to investigate its morphology. Many automatic neuron tracing methods have been proposed, but without manual check it is difficult to know whether a reconstruction or which substructure in a reconstruction is accurate. For a neuron's reconstructions generated by multiple automatic tracing methods with different principles or models, their common substructures are highly reliable and named individual motifs. In this work, we propose a Vaa3D-based method called Lamotif to explore individual motifs in automatic reconstructions of a neuron. Lamotif utilizes the local alignment algorithm in BlastNeuron to extract local alignment pairs between a specified objective reconstruction and multiple reference reconstructions, and combines these pairs to generate individual motifs on the objective reconstruction. The proposed Lamotif is evaluated on reconstructions of 163 multiple species neurons, which are generated by four state-of-the-art tracing methods. Experimental results show that individual motifs are almost on corresponding gold standard reconstructions and have much higher precision rate than objective reconstructions themselves. Furthermore, an objective reconstruction is mostly quite accurate if its individual motifs have high recall rate. Individual motifs contain common geometry substructures in multiple reconstructions, and can be used to select some accurate substructures from a reconstruction or some accurate reconstructions from automatic reconstruction dataset of different neurons.
Collapse
Affiliation(s)
- Yishan He
- Faculty of Information Technology, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing, 100124, China.,Beijing International Collaboration Base On Brain Informatics and Wisdom Services, 100 Pingleyuan, Chaoyang District, Beijing, 100124, China
| | - Jiajin Huang
- Faculty of Information Technology, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing, 100124, China.,Beijing International Collaboration Base On Brain Informatics and Wisdom Services, 100 Pingleyuan, Chaoyang District, Beijing, 100124, China
| | - Gaowei Wu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, 19(A) Yuquan Road, Shijingshan District, Beijing, 100049, China.,Institute of Automation, Chinese Academy of Sciences, Haidian District, 95 Zhongguancun East Road, Beijing, 100190, China
| | - Jian Yang
- Faculty of Information Technology, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing, 100124, China. .,Beijing International Collaboration Base On Brain Informatics and Wisdom Services, 100 Pingleyuan, Chaoyang District, Beijing, 100124, China. .,School of Artificial Intelligence, University of Chinese Academy of Sciences, 19(A) Yuquan Road, Shijingshan District, Beijing, 100049, China.
| |
Collapse
|
7
|
Zhou H, Li S, Li A, Huang Q, Xiong F, Li N, Han J, Kang H, Chen Y, Li Y, Lin H, Zhang YH, Lv X, Liu X, Gong H, Luo Q, Zeng S, Quan T. GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population. Neuroinformatics 2021; 19:305-317. [PMID: 32844332 DOI: 10.1007/s12021-020-09484-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem. GTree offers an error-screening system for the fast localization of submicron errors in densely packed neurites and along with long projections across the whole brain, thus achieving reconstruction close to the ground truth. Moreover, GTree integrates a series of our previous algorithms to significantly reduce manual interference and achieve high-level automation. When applied to an entire mouse brain dataset, GTree is shown to be five times faster than widely used commercial software. Finally, using GTree, we demonstrate the reconstruction of 35 long-projection neurons around one injection site of a mouse brain. GTree is also applicable to large datasets (10 TB or higher) from various light microscopes.
Collapse
Affiliation(s)
- Hang Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Shiwei Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Qing Huang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Feng Xiong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Ning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Jiacheng Han
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Hongtao Kang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Yijun Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Yun Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Huimin Lin
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Yu-Hui Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Xiaohua Lv
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Xiuli Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China. .,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China. .,School of Mathematics and Economics, Hubei University of Education, 430205, Wuhan, Hubei, China.
| |
Collapse
|
8
|
Yang J, He Y, Liu X. Retrieving similar substructures on 3D neuron reconstructions. Brain Inform 2020; 7:14. [PMID: 33146802 PMCID: PMC7642183 DOI: 10.1186/s40708-020-00117-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 10/26/2020] [Indexed: 11/16/2022] Open
Abstract
Since manual tracing is time consuming and the performance of automatic tracing is unstable, it is still a challenging task to generate accurate neuron reconstruction efficiently and effectively. One strategy is generating a reconstruction automatically and then amending its inaccurate parts manually. Aiming at finding inaccurate substructures efficiently, we propose a pipeline to retrieve similar substructures on one or more neuron reconstructions, which are very similar to a marked problematic substructure. The pipeline consists of four steps: getting a marked substructure, constructing a query substructure, generating candidate substructures and retrieving most similar substructures. The retrieval procedure was tested on 163 gold standard reconstructions provided by the BigNeuron project and a reconstruction of a mouse’s large neuron. Experimental results showed that the implementation of the proposed methods is very efficient and all retrieved substructures are very similar to the marked one in numbers of nodes and branches, and degree of curvature.
Collapse
Affiliation(s)
- Jian Yang
- Faculty of Information Technology, Beijing University of Technology, Beijing, China. .,Beijing International Collaboration Base On Brain Informatics and Wisdom Services, Beijing, China. .,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
| | - Yishan He
- Faculty of Information Technology, Beijing University of Technology, Beijing, China.,Beijing International Collaboration Base On Brain Informatics and Wisdom Services, Beijing, China
| | - Xuefeng Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China.,Beijing International Collaboration Base On Brain Informatics and Wisdom Services, Beijing, China
| |
Collapse
|
9
|
Huang Q, Chen Y, Liu S, Xu C, Cao T, Xu Y, Wang X, Rao G, Li A, Zeng S, Quan T. Weakly Supervised Learning of 3D Deep Network for Neuron Reconstruction. Front Neuroanat 2020; 14:38. [PMID: 32848636 PMCID: PMC7399060 DOI: 10.3389/fnana.2020.00038] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/05/2020] [Indexed: 11/13/2022] Open
Abstract
Digital reconstruction or tracing of 3D tree-like neuronal structures from optical microscopy images is essential for understanding the functionality of neurons and reveal the connectivity of neuronal networks. Despite the existence of numerous tracing methods, reconstructing a neuron from highly noisy images remains challenging, particularly for neurites with low and inhomogeneous intensities. Conducting deep convolutional neural network (CNN)-based segmentation prior to neuron tracing facilitates an approach to solving this problem via separation of weak neurites from a noisy background. However, large manual annotations are needed in deep learning-based methods, which is labor-intensive and limits the algorithm's generalization for different datasets. In this study, we present a weakly supervised learning method of a deep CNN for neuron reconstruction without manual annotations. Specifically, we apply a 3D residual CNN as the architecture for discriminative neuronal feature extraction. We construct the initial pseudo-labels (without manual segmentation) of the neuronal images on the basis of an existing automatic tracing method. A weakly supervised learning framework is proposed via iterative training of the CNN model for improved prediction and refining of the pseudo-labels to update training samples. The pseudo-label was iteratively modified via mining and addition of weak neurites from the CNN predicted probability map on the basis of their tubularity and continuity. The proposed method was evaluated on several challenging images from the public BigNeuron and Diadem datasets, to fMOST datasets. Owing to the adaption of 3D deep CNNs and weakly supervised learning, the presented method demonstrates effective detection of weak neurites from noisy images and achieves results similar to those of the CNN model with manual annotations. The tracing performance was significantly improved by the proposed method on both small and large datasets (>100 GB). Moreover, the proposed method proved to be superior to several novel tracing methods on original images. The results obtained on various large-scale datasets demonstrated the generalization and high precision achieved by the proposed method for neuron reconstruction.
Collapse
Affiliation(s)
- Qing Huang
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yijun Chen
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Shijie Liu
- School of Mathematics and Physics, China University of Geosciences, Wuhan, China
| | - Cheng Xu
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tingting Cao
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yongchao Xu
- School of Electronics Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaojun Wang
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Gong Rao
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Anan Li
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Shaoqun Zeng
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tingwei Quan
- Wuhan National Laboratory for Optoelectronics-Huazhong, Britton Chance Center for Biomedical Photonics, University of Science and Technology, Wuhan, China
- Ministry of Education (MoE) Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
10
|
FMST: an Automatic Neuron Tracing Method Based on Fast Marching and Minimum Spanning Tree. Neuroinformatics 2019; 17:185-196. [PMID: 30039210 DOI: 10.1007/s12021-018-9392-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Neuron reconstruction is an important technique in computational neuroscience. Although there are many reconstruction algorithms, few can generate robust results. In this paper, we propose a reconstruction algorithm called fast marching spanning tree (FMST). FMST is based on a minimum spanning tree method (MST) and improve its performance in two aspects: faster implementation and no loss of small branches. The contributions of the proposed method are as follows. Firstly, the Euclidean distance weight of edges in MST is improved to be a more reasonable value, which is related to the probability of the existence of an edge. Secondly, a strategy of pruning nodes is presented, which is based on the radius of a node's inscribed ball. Thirdly, separate branches of broken neuron reconstructions can be merged into a single tree. FMST and many other state of the art reconstruction methods were implemented on two datasets: 120 Drosophila neurons and 163 neurons with gold standard reconstructions. Qualitative and quantitative analysis on experimental results demonstrates that the performance of FMST is good compared with many existing methods. Especially, on the 91 fruitfly neurons with gold standard and evaluated by five metrics, FMST is one of two methods with best performance among all 27 state of the art reconstruction methods. FMST is a good and practicable neuron reconstruction algorithm, and can be implemented in Vaa3D platform as a neuron tracing plugin.
Collapse
|
11
|
Li S, Quan T, Zhou H, Huang Q, Guan T, Chen Y, Xu C, Kang H, Li A, Fu L, Luo Q, Gong H, Zeng S. Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method. Neuroinformatics 2019; 18:199-218. [PMID: 31396858 DOI: 10.1007/s12021-019-09434-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Neuronal shape reconstruction is a helpful technique for establishing neuron identity, inferring neuronal connections, mapping neuronal circuits, and so on. Advances in optical imaging techniques have enabled data collection that includes the shape of a neuron across the whole brain, considerably extending the scope of neuronal anatomy. However, such datasets often include many fuzzy neurites and many crossover regions that neurites are closely attached, which make neuronal shape reconstruction more challenging. In this study, we proposed a convex image segmentation model for neuronal shape reconstruction that segments a neurite into cross sections along its traced skeleton. Both the sparse nature of gradient images and the rule that fuzzy neurites usually have a small radius are utilized to improve neuronal shape reconstruction in regions with fuzzy neurites. Because the model is closely related to the traced skeleton point, we can use this relationship for identifying neurite with crossover regions. We demonstrated the performance of our model on various datasets, including those with fuzzy neurites and neurites with crossover regions, and we verified that our model could robustly reconstruct the neuron shape on a brain-wide scale.
Collapse
Affiliation(s)
- Shiwei Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China. .,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China. .,School of Mathematics and Economics, Hubei University of Education, Wuhan, 430205, Hubei, China.
| | - Hang Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Qing Huang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Tao Guan
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yijun Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Cheng Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Hongtao Kang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| |
Collapse
|
12
|
Li S, Quan T, Xu C, Huang Q, Kang H, Chen Y, Li A, Fu L, Luo Q, Gong H, Zeng S. Optimization of Traced Neuron Skeleton Using Lasso-Based Model. Front Neuroanat 2019; 13:18. [PMID: 30846931 PMCID: PMC6393391 DOI: 10.3389/fnana.2019.00018] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 02/01/2019] [Indexed: 11/30/2022] Open
Abstract
Reconstruction of neuronal morphology from images involves mainly the extraction of neuronal skeleton points. It is an indispensable step in the quantitative analysis of neurons. Due to the complex morphology of neurons, many widely used tracing methods have difficulties in accurately acquiring skeleton points near branch points or in structures with tortuosity. Here, we propose two models to solve these problems. One is based on an L1-norm minimization model, which can better identify tortuous structure, namely, a local structure with large curvature skeleton points; the other detects an optimized branch point by considering the combination patterns of all neurites that link to this point. We combined these two models to achieve optimized skeleton detection for a neuron. We validate our models in various datasets including MOST and BigNeuron. In addition, we demonstrate that our method can optimize the traced skeletons from large-scale images. These characteristics of our approach indicate that it can reduce manual editing of traced skeletons and help to accelerate the accurate reconstruction of neuronal morphology.
Collapse
Affiliation(s)
- Shiwei Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China.,School of Mathematics and Economics, Hubei University of Education, Hubei, China
| | - Cheng Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
| | - Qing Huang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
| | - Hongtao Kang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
| | - Yijun Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
| | - Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Hubei, China
| |
Collapse
|
13
|
Li S, Quan T, Zhou H, Yin F, Li A, Fu L, Luo Q, Gong H, Zeng S. Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites. Neuroinformatics 2019; 17:497-514. [PMID: 30635864 PMCID: PMC6841657 DOI: 10.1007/s12021-018-9414-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the whole brain. However, high-level automation reconstruction of neurons, i.e., the reconstruction with a few of manual edits requires discrimination of weak foreground points from the inhomogeneous background. We constructed an identification model, where empirical observations made from neuronal images were summarized into rules for designing feature vectors that to classify foreground and background, and a support vector machine (SVM) was used to learn these feature vectors. We embedded this constructed SVM classifier into a previously developed tool, SparseTracer, to obtain SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace sparsely distributed neurites with weak signals (contrast-to-noise ratio < 1.5) against an inhomogeneous background in datasets imaged by widely used light-microscopy techniques like confocal microscopy and two-photon microscopy. Moreover, 12 sub-blocks were extracted from different brain regions. The average recall and precision rates were 99% and 97%, respectively. These results indicated that ST-LFV is well suited for weak signal identification with varying image characteristics. We also applied ST-LFV to trace long-range neurites from images where neurites are sparsely distributed but their image intensities are weak in some cases. When tracing this long-range neurites, manual edit was required once to obtain results equivalent to the ground truth, compared with 20 times of manual edits required by SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the large scale.
Collapse
Affiliation(s)
- Shiwei Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China. .,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China. .,School of Mathematics and Economics, Hubei University of Education, Wuhan, 430205, Hubei, China.
| | - Hang Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - FangFang Yin
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| |
Collapse
|
14
|
Skibbe H, Reisert M, Nakae K, Watakabe A, Hata J, Mizukami H, Okano H, Yamamori T, Ishii S. PAT-Probabilistic Axon Tracking for Densely Labeled Neurons in Large 3-D Micrographs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:69-78. [PMID: 30010551 DOI: 10.1109/tmi.2018.2855736] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A major goal of contemporary neuroscience research is to map the structural connectivity of mammalian brain using microscopy imaging data. In this context, the reconstruction of densely labeled axons from two-photon microscopy images is a challenging and important task. The visually overlapping, crossing, and often strongly distorted images of the axons allow many ambiguous interpretations to be made. We address the problem of tracking axons in densely labeled samples of neurons in large image data sets acquired from marmoset brains. Our high-resolution images were acquired using two-photon microscopy and they provided whole brain coverage, occupying terabytes of memory. Both the image distortions and the large data set size frequently make it impractical to apply present-day neuron tracing algorithms to such data due to the optimization of such algorithms to the precise tracing of either single or sparse sets of neurons. Thus, new tracking techniques are needed. We propose a probabilistic axon tracking algorithm (PAT). PAT tackles the tracking of axons in two steps: locally (L-PAT) and globally (G-PAT). L-PAT is a probabilistic tracking algorithm that can tackle distorted, cluttered images of densely labeled axons. L-PAT divides a large micrograph into smaller image stacks. It then processes each image stack independently before mapping the axons in each image to a sparse model of axon trajectories. G-PAT merges the sparse L-PAT models into a single global model of axon trajectories by minimizing a global objective function using a probabilistic optimization method. We demonstrate the superior performance of PAT over standard approaches on synthetic data. Furthermore, we successfully apply PAT to densely labeled axons in large images acquired from marmoset brains.
Collapse
|
15
|
Liu S, Zhang D, Song Y, Peng H, Cai W. Automated 3-D Neuron Tracing With Precise Branch Erasing and Confidence Controlled Back Tracking. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2441-2452. [PMID: 29993997 DOI: 10.1109/tmi.2018.2833420] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The automatic reconstruction of single neurons from microscopic images is essential to enable large-scale data-driven investigations in neuron morphology research. However, few previous methods were able to generate satisfactory results automatically from 3-D microscopic images without human intervention. In this paper, we developed a new algorithm for automatic 3-D neuron reconstruction. The main idea of the proposed algorithm is to iteratively track backward from the potential neuronal termini to the soma centre. An online confidence score is computed to decide if a tracing iteration should be stopped and discarded from the final reconstruction. The performance improvements comparing with the previous methods are mainly introduced by a more accurate estimation of the traced area and the confidence controlled back-tracking algorithm. The proposed algorithm supports large-scale batch-processing by requiring only one user specified parameter for background segmentation. We bench tested the proposed algorithm on the images obtained from both the DIADEM challenge and the BigNeuron challenge. Our proposed algorithm achieved the state-of-the-art results.
Collapse
|
16
|
Gang Y, Liu X, Wang X, Zhang Q, Zhou H, Chen R, Liu L, Jia Y, Yin F, Rao G, Chen J, Zeng S. Plastic embedding immunolabeled large-volume samples for three-dimensional high-resolution imaging. BIOMEDICAL OPTICS EXPRESS 2017; 8:3583-3596. [PMID: 28856037 PMCID: PMC5560827 DOI: 10.1364/boe.8.003583] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 06/28/2017] [Accepted: 06/28/2017] [Indexed: 05/31/2023]
Abstract
High-resolution three-dimensional biomolecule distribution information of large samples is essential to understanding their biological structure and function. Here, we proposed a method combining large sample resin embedding with iDISCO immunofluorescence staining to acquire the profile of biomolecules with high spatial resolution. We evaluated the compatibility of plastic embedding with an iDISCO staining technique and found that the fluorophores and the neuronal fine structures could be well preserved in the Lowicryl HM20 resin, and that numerous antibodies and fluorescent tracers worked well upon Lowicryl HM20 resin embedding. Further, using fluorescence Micro-Optical sectioning tomography (fMOST) technology combined with ultra-thin slicing and imaging, we were able to image the immunolabeled large-volume tissues with high resolution.
Collapse
Affiliation(s)
- Yadong Gang
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
- These authors contributed equally to this work
| | - Xiuli Liu
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
- These authors contributed equally to this work
| | - Xiaojun Wang
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
| | - Qi Zhang
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
| | - Hongfu Zhou
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
| | - Ruixi Chen
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
| | - Ling Liu
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
| | - Yao Jia
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
| | - Fangfang Yin
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
| | - Gong Rao
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
| | - Jiadong Chen
- Department of Cell Biology and Program in Molecular Cell Biology, Key Laboratory of Medical Neurobiology of the Ministry of Health of China
- Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang 310058, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MOE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China
| |
Collapse
|