# Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1707.06314 · HTML: https://mlsanity.com/paper/1707.06314/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [Deep and shallow data science for multi-scale optical neuroscience](https://mlsanity.com/paper/2402.08811/) (2024)
- [Automatic Neuronal Activity Segmentation in Fast Four Dimensional Spatio-Temporal Fluorescence Imaging using Bayesian Approach](https://mlsanity.com/paper/2512.19032/) (2025)

## Abstract (arXiv)

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 512×512 images at ≈9K images per minute. It ranks third in the Neurofinder competition (F1=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.