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Abstract
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 three-dimensional morphology of single neurons based on branch-specific structural features. Applying NeuroRetriever to automatically segment single neurons in 22,037 raw brain images, we successfully retrieved 28,125 individual neurons validated by human segmentation. Thus, automated NeuroRetriever will greatly accelerate 3D reconstruction of the single neurons for constructing the complete connectomes.
Highlights
To accomplish the goal mentioned above, we (1) developed a tracing algorithm named the Fast Automated Structure Tracing (FAST) algorithm that extracts the details of a singleneuron skeleton with high accuracy and efficiency at multiple intensity thresholds; (2) introduced the concept of “branch robustness score” (BRS) based on domain knowledge of neuronal morphology to assess the position of each voxel within the structure; (3) adapted an high dynamic range (HDR) thresholding mask on the basis of BRS to segment the target neuron; and (4) integrated the algorithms above into an executable package named NeuroRetriever (NR), which automatically segments and reconstructs a large population of fluorescent single-neuron images for connectome assembly
With the HDR thresholding mask containing a wide range of intensity thresholds, the program automatically segmented the single neuron by intersecting between the mask and the raw image
We report an automatic algorithm, NeuroRetriever, using anatomic features and HDR thresholding to segment single neurons directly from the raw fluorescent images with variable background noises
Summary
To accomplish the goal mentioned above, we (1) developed a tracing algorithm named the Fast Automated Structure Tracing (FAST) algorithm that extracts the details of a singleneuron skeleton with high accuracy and efficiency at multiple intensity thresholds (or level sets); (2) introduced the concept of “branch robustness score” (BRS) based on domain knowledge of neuronal morphology to assess the position of each voxel within the structure; (3) adapted an HDR thresholding mask on the basis of BRS to segment the target neuron; and (4) integrated the algorithms above into an executable package named NeuroRetriever (NR), which automatically segments and reconstructs a large population of fluorescent single-neuron images for connectome assembly. The squares represent voxels and the number in each square represents its green fluorescent protein (GFP) intensity In this case, a global intensity threshold, t = 4, was applied. Upon applying thresholds greater than the intensity of such weak points, they would be eliminated and downstream branches would be excluded from the segmentation. This essential information arose from the FAST tracing results.
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