DeepACSON automated segmentation of white matter in 3D electron microscopy.
Journal:
Communications biology
Published Date:
Feb 10, 2021
Abstract
Tracing the entirety of ultrastructures in large three-dimensional electron microscopy (3D-EM) images of the brain tissue requires automated segmentation techniques. Current segmentation techniques use deep convolutional neural networks (DCNNs) and rely on high-contrast cellular membranes and high-resolution EM volumes. On the other hand, segmenting low-resolution, large EM volumes requires methods to account for severe membrane discontinuities inescapable. Therefore, we developed DeepACSON, which performs DCNN-based semantic segmentation and shape-decomposition-based instance segmentation. DeepACSON instance segmentation uses the tubularity of myelinated axons and decomposes under-segmented myelinated axons into their constituent axons. We applied DeepACSON to ten EM volumes of rats after sham-operation or traumatic brain injury, segmenting hundreds of thousands of long-span myelinated axons, thousands of cell nuclei, and millions of mitochondria with excellent evaluation scores. DeepACSON quantified the morphology and spatial aspects of white matter ultrastructures, capturing nanoscopic morphological alterations five months after the injury.
Authors
Keywords
Animals
Artificial Intelligence
Brain Injuries, Traumatic
Cell Nucleus
Disease Models, Animal
Image Interpretation, Computer-Assisted
Imaging, Three-Dimensional
Male
Microscopy, Electron
Mitochondria
Nerve Fibers, Myelinated
Predictive Value of Tests
Rats
Rats, Sprague-Dawley
Reproducibility of Results
White Matter