Abstract
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and 'straightened' freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90-100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze.
Highlights
The brain is a complex system consisting of thousands of millions of neurons that interact with each other in a highly organized way [1,2,3,4]
Our results demonstrated that deep learning could be used to establish a flexible method for extracting neuronal activities; such algorithms could be used by more laboratories performing whole-brain calcium imaging without considerable modification
Two types of 3D images were simultaneously scanned: 1. the neuron nucleus markers, and 2 the calcium indicators, which measured the concentration of the calcium in each neuron, reflecting neuronal activities
Summary
The brain is a complex system consisting of thousands of millions of neurons that interact with each other in a highly organized way [1,2,3,4]. To understand how this complex system works, we need to monitor whole-brain neuronal activity on living animals [5,6] Traditional electrophysiological techniques such as multichannel extracellular recording can only measure the simultaneous activities of a small proportion of neurons. While these types of studies can be used to elucidate local features of specific neurons, they cannot help us understand the brain as a whole. Other techniques such as electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) can monitor whole brain activities. These techniques have been used in small animals with transparent brains, such as larval zebrafish [9,10,11,12,13] and the nematode Caenorhabditis elegans [14,15,16,17,18]
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