Abstract
Axonal structure underlies white matter functionality and plays a major role in brain connectivity. The current literature on the axonal structure is based on the analysis of two-dimensional (2D) cross-sections, which, as we demonstrate, is precarious. To be able to quantify three-dimensional (3D) axonal morphology, we developed a novel pipeline, called ACSON (AutomatiC 3D Segmentation and morphometry Of axoNs), for automated 3D segmentation and morphometric analysis of the white matter ultrastructure. The automated pipeline eliminates the need for time-consuming manual segmentation of 3D datasets. ACSON segments myelin, myelinated and unmyelinated axons, mitochondria, cells and vacuoles, and analyzes the morphology of myelinated axons. We applied the pipeline to serial block-face scanning electron microscopy images of the corpus callosum of sham-operated (n = 2) and brain injured (n = 3) rats 5 months after the injury. The 3D morphometry showed that cross-sections of myelinated axons were elliptic rather than circular, and their diameter varied substantially along their longitudinal axis. It also showed a significant reduction in the myelinated axon diameter of the ipsilateral corpus callosum of rats 5 months after brain injury, indicating ongoing axonal alterations even at this chronic time-point.
Highlights
Electron microscopy (EM) techniques are used extensively to assess brain tissue ultrastructure
We devised the ACSON segmentation pipeline to annotate the ultrastructure in SBEM volumes of white matter
Previous studies that quantified white matter were limited to 2D morphometry, which simplifies the assumptions about axonal morphology
Summary
Electron microscopy (EM) techniques are used extensively to assess brain tissue ultrastructure. Several software tools have been developed that focus on either manual annotation (e.g., KNOSSOS12, TrakEM213, Microscopy Image Browser[14], and CATMAID15), or interactive processing of data by combining automated analysis and proof-reading capabilities (e.g., rhoANA16, ilastik[17], and SegEM18). In addition to these software tools, a variety of studies have proposed segmentation pipelines for analyzing large amounts of TEM data. The automated segmentation of SBEM images of white matter requires a developed method to address these problems. We analyzed the morphological features of SBEM datasets from the ipsilateral and contralateral sides of the corpus callosum in two sham-operated and three TBI rats
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