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Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy.
The proposed dense transformer modules are differentiable, thus the entire network can be trained.
Most current state-of-the-art connectome reconstruction pipelines have two major steps: initial pixel-based segmentation with affinity prediction and watershed transform, and refined segmentation by merging over-segmented regions.
SOTA for Electron Microscopy Image Segmentation on SNEMI3D (Total Variation of Information metric )
Both empirically and theoretically, it is unclear whether or when deep metric learning is superior to the more conventional approach of directly predicting an affinity graph with a convolutional net.
Reconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites.
In this paper, we propose a novel strategy to apply such segmentation on very large datasets that exceed the capacity of a single machine.
To build the connectomics map of the brain, we developed a new algorithm that can automatically refine the Membrane Detection Probability Maps (MDPM) generated to perform automatic segmentation of electron microscopy (EM) images.