NeuroRetriever: Automatic Neuron Segmentation for Connectome Assembly - Shih, Chi-Tin; Chen, Nan-Yow; Wang, Ting-Yuan; He, Guan-Wei; Wang, Guo-Tzau; Lin, Yen-Jen; Lee, Ting-Kuo; Chiang, Ann-Shyn (Frontiers in Systems Neuroscience, 2021)
Segmenting individual neurons from a large number of noisy raw images is the first step in building a comprehensive map of neuron-to-neuron connections for predicting information flow in the brain. Thousands of fluorescence-labeled brain neurons have been imaged. However, mapping a complete connectome remains challenging because imaged neurons are often entangled and manual segmentation of a large population of single neurons is laborious and prone to bias. In this study, we report an automatic algorithm, NeuroRetriever, for unbiased large-scale segmentation of confocal fluorescence images of single neurons in the adult Drosophila brain. NeuroRetriever uses a high-dynamic-range thresholding method to segment single neurons based on branch-specific structural features. With high quality brain images available nowadays, NeuroRetriever allows us to successfully retrieve 28,125 single-neuron images from 22,037 raw brain images validated by human segmentation. Thus, automated NeuroRetriever will greatly accelerate 3D reconstruction of the single neurons for constructing the complete connectomes.