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.
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
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- 1.
See discussion: https://github.com/codeneuro/neurofinder/issues/25.
- 2.
Details and implementation: https://github.com/codeneuro/neurofinder-python.
- 3.
From the website: For the 00 data, labels are derived from an anatomical marker that indicates the precise location of each neuron and includes neurons with no activity. For the 01, 02, 03, 04 data, labels were hand drawn or manually curated, using the raw data and various summary statistics, some of which are biased towards active neurons.
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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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Klibisz, A., Rose, D., Eicholtz, M., Blundon, J., Zakharenko, S. (2017). Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_33
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