Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

  • Aleksander Klibisz
  • Derek Rose
  • Matthew Eicholtz
  • Jay Blundon
  • Stanislav Zakharenko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)

Abstract

Calcium imaging is a technique for observing neuron activity as a series of images showing indicator fluorescence over time. Manually segmenting neurons is time-consuming, leading to research on automated calcium imaging segmentation (ACIS). We evaluated several deep learning models for ACIS on the Neurofinder competition datasets and report our best model: U-Net2DS, a fully convolutional network that operates on 2D mean summary images. U-Net2DS requires minimal domain-specific pre/post-processing and parameter adjustment, and predictions are made on full \(512\,\times \,512\) images at \(\approx \)9K images per minute. It ranks third in the Neurofinder competition (\(F_1=0.57\)) and is the best model to exclusively use deep learning. We also demonstrate useful segmentations on data from outside the competition. The model’s simplicity, speed, and quality results make it a practical choice for ACIS and a strong baseline for more complex models in the future.

Keywords

Calcium imaging Fully convolutional networks Deep learning Microscopy segmentation 

Notes

Acknowledgments

This work was supported in part by the Department of Developmental Neurobiology at St. Jude Children’s Research Hospital and by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internship program.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aleksander Klibisz
    • 1
  • Derek Rose
    • 1
  • Matthew Eicholtz
    • 1
  • Jay Blundon
    • 2
  • Stanislav Zakharenko
    • 2
  1. 1.Oak Ridge National LaboratoryOak RidgeUSA
  2. 2.St. Jude Children’s Research HospitalMemphisUSA

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