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Image Segmentation for Connectomics Using Machine Learning

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Computational Intelligence in Biomedical Imaging

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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.

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Notes

  1. 1.

    According to the “rule-of-thumb” in [90], one needs at least 10 ×training samples of the total number of parameters. Thus, compared to Jain et al. [39] convolutional ANN, the approach presented here needs about 27 ×less training samples, for the values given.

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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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Authors and Affiliations

  1. Electrical and Computer Engineering Department, Scientific Computing and Imaging Institute, University of Utah, Lake City, UT, 84112, USA

    T. Tasdizen, M. Seyedhosseini & C. Jones

  2. School of Computing, Scientific Computing and Imaging Institute, University of Utah, Lake City, UT, 84112, USA

    T. Liu & E. Jurrus

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  1. T. Tasdizen
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Correspondence to T. Tasdizen .

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  1. Department of Radiology, University of Chicago, Chicago, Illinois, USA

    Kenji Suzuki

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Tasdizen, T., Seyedhosseini, M., Liu, T., Jones, C., Jurrus, E. (2014). Image Segmentation for Connectomics Using Machine Learning. In: Suzuki, K. (eds) Computational Intelligence in Biomedical Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7245-2_10

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