Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron... Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

Download PDF
25 pages

Loading next page...
 
/lp/springer-journals/semi-automated-neuron-boundary-detection-and-nonbranching-process-Qko1ZDRXLh
Publisher
Springer Journals
Copyright
Copyright © 2012 by Springer Science+Business Media, LLC
Subject
Biomedicine; Neurosciences; Bioinformatics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Neurology
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1007/s12021-012-9149-y
pmid
22644867
Publisher site
See Article on Publisher Site

Abstract

Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.

Journal

Neuroinformatics – Springer Journals

Published: May 30, 2012

References

  • CARTOS II semi-automated nerve tracing: Three-dimensional reconstruction from serial section micrographs
    Allen, BA; Levinthal, C
  • Exploring the retinal connectome
    Anderson, JR; Jones, BW; Watt, CB; Shaw, MV; Yang, JH; Demill, D
  • A computational framework for ultrastructural mapping of neural circuitry
    Anderson, JR; Jones, BW; Yang, J-H; Shaw, MV; Watt, CB; Koshevoy, P
  • Pattern recognition
    Andres, B; Köthe, U; Helmstaedter, M; Denk, W; Hamprecht, FA
  • Morphing active contours
    Bertalmío, M; Sapiro, G; Randall, G
  • Imaging intracellular fluorescent proteins at nanometer resolution
    Betzig, E; Patterson, GH; Sougrat, R; Lindwasser, OW; Olenych, S; Bonifacino, JS
  • Towards neural circuit reconstruction with volume electron microscopy techniques
    Briggman, KL; Denk, W
  • Towards neural circuit reconstruction with volume electron microscopy techniques
    Briggman, KL; Denk, W
  • An integrated micro- and macroarchitectural analysis of the Drosophila brain by computer-assisted serial section electron microscopy
    Cardona, A; Saalfeld, S; Preibisch, S; Schmid, B; Cheng, A; Pulokas, J
  • Semi-automated reconstruction of neural circuits using electron microscopy
    Chklovskii, DB; Vitaladevuni, S; Scheffer, LK
  • Extracting features from faces using compression networks: Face, identity, emotion and gender recognition using holons
    Cottrell, GW
  • Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure
    Denk, W; Horstmann, H
  • Two-photon laser scanning microscopy
    Denk, W; Strickler, JH; Webb, WW
  • Fluorescence microscopy with super-resolved optical sections
    Egner, A; Hell, SW
  • Extending unbiased stereology of brain ultrastructure to three-dimensional volumes
    Fiala, JC; Harris, KM
  • Computer-based alignment and reconstruction of serial sections
    Fiala, JC; Harris, KM
  • Digital image processing
    Gonzalez, RC; Woods, RE
  • Structure-function relationship depends on glaucoma severity
    Gonzalez-Hernandez, M; Pablo, LE; Armas-Dominguez, K; Rodriguez de la Vega, R; Ferreras, A; Gonzalez de la Rosa, M
  • Neural networks—A comprehensive foundation
    Haykin, S
  • Machines that learn to segment images: A crucial technology for connectomics
    Jain, V; Sebastian Seung, H; Turaga, SC
  • Ssecrett and neurotrace: Interactive visualization and analysis tools for large-scale neuroscience data sets
    Jeong, W-K; Beyer, J; Hadwiger, M; Blue, R; Law, C; Vazquez-Reina, A
  • Scalable and interactive segmentation and visualization of neural processes in EM datasets
    Jeong, W-K; Beyer, J; Hadwiger, M; Vazquez, A; Pfister, H; Whitaker, RT
  • Control of type-D GABAergic neuron differentiation by C. elegans UNC-30 homeodomain protein
    Jin, Y; Hoskins, R; Horvitz, HR
  • Retinal remodeling during retinal degeneration
    Jones, BW; Marc, RE
  • Retinal remodeling triggered by photoreceptor degenerations
    Jones, BW; Watt, CB; Frederick, JM; Baehr, W; Chen, CK; Levine, EM
  • Retinal remodelling
    Jones, BW; Watt, CB; Marc, RE
  • Axon tracking in serial block-face scanning electron microscopy
    Jurrus, E; Hardy, M; Tasdizen, T; Fletcher, PT; Koshevoy, P; Chien, C-B
  • Detection of neuron membranes in electron microscopy images using a serial neural network architecture
    Jurrus, E; Paiva, ARC; Watanabe, S; Anderson, JR; Jones, BW; Whitaker, RT
  • Computer visualization of three-dimensional image data using imod
    Kremer, JR; Mastronarde, DN; McIntosh, JR
  • Contour-propagation algorithms for semi-automated reconstruction of neural processes
    Macke, JH; Maack, N; Gupta, R; Denk, W; Schölkopf, B; Borst, A
  • Neural reprogramming in retinal degeneration
    Marc, RE; Jones, BW; Anderson, JR; Kinard, K; Marshak, DW; Wilson, JH
  • Neural remodeling in retinal degeneration
    Marc, RE; Jones, BW; Watt, CB; Strettoi, E
  • Extreme retinal remodeling triggered by light damage: Implications for age related macular degeneration
    Marc, RE; Jones, BW; Watt, CB; Vazquez-Chona, F; Vaughan, DK; Organisciak, DT
  • The cell centered database project: An update on building community resources for managing and sharing 3D imaging data
    Martone, ME; Tran, J; Wong, WW; Sargis, J; Fong, L; Larson, S; Lamont, SP
  • Computational framework for segmentation and grouping
    Medioni, G; Lee, M-S; Tang, C-K
  • Ultrastructural analysis of hippocampal neuropil from the connectomics perspective
    Mishchenko, Y; Hu, T; Spacek, J; Mendenhall, J; Harris, KM; Chklovskii, DB
  • Ectopic synaptogenesis in the mammalian retina caused by rod photoreceptor-specific mutations
    Peng, YW; Hao, Y; Petters, RM; Wong, F
  • Robot learning
    Pomerleau, D
  • Neural and adaptive systems: Fundamentals through simulations
    Principe, JC; Euliano, NR; Lefebvre, WC
  • Visual speech recognition by recurrent neural networks
    Rabi, G; Lu, SW
  • Objective criteria for the evaluation of clustering methods
    Rand, WM
  • Nonrigid registration using free-form deformations: Application to breast MR images
    Rueckert, D; Sonoda, LI; Hayes, C; Hill, DLG; Leach, MO; Hawkes, DJ
  • Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (storm)
    Rust, MJ; Bates, M; Zhuang, X
  • As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets
    Saalfeld, S; Cardona, A; Hartenstein, V; Tomančák, P
  • Automatic mosaicking and volume assembly for high-throughput serial-section transmission electron microscopy
    Tasdizen, T; Koshevoy, P; Grimm, BC; Anderson, JR; Jones, BW; Watt, CB
  • Convolutional networks can learn to generate affinity graphs for image segmentation
    Turaga, SC; Murray, JF; Jain, V; Roth, F; Helmstaedter, M; Briggman, K
  • Structural properties of the Caenorhabditis elegans neuronal network
    Varshney, LR; Chen, BL; Paniagua, E; Hall, DH; Chklovskii, DB
  • Three-dimensional organization of the perivascular glial limiting membrane and its relationship with the vasculature: A scanning electron microscope study
    Watanabe, K; Takeishi, H; Hayakawa, T; Sasaki, H
  • Promising research: Vision-based robot positioning using neural networks
    Wells, G; Venaille, C; Torras, C
  • The sensory circuitry for sexual attraction in C. elegans males
    White, JQ; Nicholas, TJ; Gritton, J; Truong, L; Davidson, ER; Jorgensen, EM
  • The structure of the nervous system of the nematode Caenorhabditis elegans
    White, JG; Southgate, E; Thomson, JN; Brenner, FRS
  • A spatially organized representation of colour in macaque cortical area v2
    Xiao, YP; Wang, Y; Felleman, DJ