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SegEM: Efficient Image Analysis for High-Resolution Connectomics
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DOI：10.1016/j.neuron.2015.09.003.png)
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Abstract
En 中文
Progress in electron microscopy-based high-resolution connectomics is limited by data analysis throughput. Here, we present SegEM, a toolset for efficient semi-automated analysis of large-scale fully stained 3D-EM datasets for the reconstruction of neuronal circuits. By combining skeleton reconstructions of neurons with automated volume segmentations, SegEM allows the reconstruction of neuronal circuits at a work hour consumption rate of about 100-fold less than manual analysis and about 10-fold less than existing segmentation tools. SegEM provides a robust classifier selection procedure for finding the best automated image classifier for different types of nerve tissue. We applied these methods to a volume of 44 x 60 x 141 mu m(3) SBEM data from mouse retina and a volume of 93 x 60 x 93 mu m(3) from mouse cortex, and performed exemplary synaptic circuit reconstruction. SegEM resolves the tradeoff between synapse detection and semi-automated reconstruction performance in high-resolution connectomics and makes efficient circuit reconstruction in fully-stained EM datasets a ready-to-use technique for neuroscience.
Keywords:
ELECTRON-MICROSCOPY
3D SEGMENTATION
RECONSTRUCTION
CIRCUIT
CHALLENGES
VOLUME
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