Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
This provides an efficient and flexible tool for neuroscientists analyzing large calcium imaging datasets, though it is incremental as it applies existing convolutional networks to a specific domain problem.
The paper tackled the problem of automatically detecting individual neurons in large-scale calcium imaging data by applying a supervised learning approach with convolutional networks, achieving near-human accuracy and superhuman speed, with superior precision and recall compared to the PCA/ICA method.
Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can achieve near-human accuracy and superhuman speed. Accuracy is superior to the popular PCA/ICA method based on precision and recall relative to ground truth annotation by a human expert. These results suggest that convolutional networks are an efficient and flexible tool for the analysis of large-scale calcium imaging data.