143 research outputs found
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 images at 9K images per minute. It
ranks third in the Neurofinder competition () 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
(http://cs.adelaide.edu.au/~dlmia3/
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