NDLI: SparseTracer: the Reconstruction of Discontinuous Neuronal Morphology in Noisy Images

Content Provider	Springer Nature Link
Author	Li, Shiwei Zhou, Hang Quan, Tingwei Li, Jing Li, Yuxin Li, Anan Luo, Qingming Gong, Hui Zeng, Shaoqun
Copyright Year	2016
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.
Starting Page	133
Ending Page	149
Page Count	17
File Format	PDF
ISSN	15392791
Journal	Neuroinformatics
Volume Number	15
Issue Number	2
e-ISSN	15590089
Language	English
Publisher	Springer US
Publisher Date	2016-12-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Digital reconstruction Automatic tracing Neuronal morphology Constrained principal curves Neurosciences Bioinformatics Computational Biology/Bioinformatics Computer Application in Life Sciences Neurology
Content Type	Text
Resource Type	Article
Subject	Neuroscience Information Systems Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Principal Curves as Skeletons of Tubular Objects : Locally Characterizing the Structures of Axons

Automated Neuron Tracing Methods: An Updated Account

Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models

Rivulet: 3D Neuron Morphology Tracing with Iterative Back-Tracking

The DIADEM Data Sets: Representative Light Microscopy Images of Neuronal Morphology to Advance Automation of Digital Reconstructions

Successes and Rewards in Sharing Digital Reconstructions of Neuronal Morphology

Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models

A Brief History of Neuronal Reconstruction

Non-parametric Algorithmic Generation of Neuronal Morphologies

SparseTracer: the Reconstruction of Discontinuous Neuronal Morphology in Noisy Images

Similar Documents

Principal Curves as Skeletons of Tubular Objects : Locally Characterizing the Structures of Axons

Automated Neuron Tracing Methods: An Updated Account

Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models

Rivulet: 3D Neuron Morphology Tracing with Iterative Back-Tracking

The DIADEM Data Sets: Representative Light Microscopy Images of Neuronal Morphology to Advance Automation of Digital Reconstructions

Successes and Rewards in Sharing Digital Reconstructions of Neuronal Morphology

Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models

A Brief History of Neuronal Reconstruction

Non-parametric Algorithmic Generation of Neuronal Morphologies

SparseTracer: the Reconstruction of Discontinuous Neuronal Morphology in Noisy Images