Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

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 $512\times512$ images at $\approx$9K images per minute. It ranks third in the Neurofinder competition ($F_1=0.569$) 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. Comment: Accepted to 3rd Workshop on Deep Learning in Medical Image Analysis (
arXiv is a free distribution service and an open-access archive for 2,162,143 scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.
Computer science
Klibisz, A., Rose, D., Eicholtz, M., Blundon, J., & Zakharenko, S. (2017). Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks.