Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks
This work addresses the time-consuming problem of manually segmenting neurons for researchers in neuroscience, though it is incremental as it builds on existing U-Net architectures.
The paper tackled automated calcium imaging segmentation by evaluating deep learning models, achieving an F1 score of 0.569 and ranking third in the Neurofinder competition with a model that processes images at about 9K per minute.
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.