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Image Segmentation for Connectomics Using Machine Learning

  • T. Tasdizen
  • M. Seyedhosseini
  • T. Liu
  • C. Jones
  • E. Jurrus
Chapter

Abstract

Reconstruction of neural circuits at the microscopic scale of individual neurons and synapses, also known as connectomics, is an important challenge for neuroscience. While an important motivation of connectomics is providing anatomical ground truth for neural circuit models, the ability to decipher neural wiring maps at the individual cell level is also important in studies of many neurodegenerative diseases. Reconstruction of a neural circuit at the individual neuron level requires the use of electron microscopy images due to their extremely high resolution. Computational challenges include pixel-by-pixel annotation of these images into classes such as cell membrane, mitochondria and synaptic vesicles and the segmentation of individual neurons. State-of-the-art image analysis solutions are still far from the accuracy and robustness of human vision and biologists are still limited to studying small neural circuits using mostly manual analysis. In this chapter, we describe our image analysis pipeline that makes use of novel supervised machine learning techniques to tackle this problem.

Keywords

Artificial Neural Network Hide Layer Active Contour Conditional Random Field Ventral Nerve Cord 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by NIH R01 EB005832 and 1R01NS075314. The C. elegans dataset was provided by the Jorgensen Lab at the University of Utah. The mouse neuropil dataset was provided by the National Center for Microscopy Imaging Research. The retina dataset was provided by the Marc Lab at the University of Utah. The drosophila VNC dataset was provided by the Cardona Lab at HHMI Janelia Farm.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • T. Tasdizen
    • 1
  • M. Seyedhosseini
    • 1
  • T. Liu
    • 2
  • C. Jones
    • 1
  • E. Jurrus
    • 2
  1. 1.Electrical and Computer Engineering Department, Scientific Computing and Imaging InstituteUniversity of UtahLake CityUSA
  2. 2.School of Computing, Scientific Computing and Imaging InstituteUniversity of UtahLake CityUSA

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