Automated tracing of myelinated axons and detection of the nodes of Ranvier in serial images of peripheral nerves
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
The development of realistic neuroanatomical models of peripheral nerves for simulation purposes requires the reconstruction of the morphology of the myelinated fibres in the nerve, including their nodes of Ranvier. Currently, this information has to be extracted by semimanual procedures, which severely limit the scalability of the experiments. In this contribution, we propose a supervised machine learning approach for the detailed reconstruction of the geometry of fibres inside a peripheral nerve based on its high-resolution serial section images. Learning from sparse expert annotations, the algorithm traces myelinated axons, even across the nodes of Ranvier. The latter are detected automatically. The approach is based on classifying the myelinated membranes in a supervised fashion, closing the membrane gaps by solving an assignment problem, and classifying the closed gaps for the nodes of Ranvier detection. The algorithm has been validated on two very different datasets: (i) rat vagus nerve subvolume, SBFSEM microscope, 200 × 200 × 200 nm resolution, (ii) rat sensory branch subvolume, confocal microscope, 384 × 384 × 800 nm resolution. For the first dataset, the algorithm correctly reconstructed 88% of the axons (241 out of 273) and achieved 92% accuracy on the task of Ranvier node detection. For the second dataset, the gap closing algorithm correctly closed 96.2% of the gaps, and 55% of axons were reconstructed correctly through the whole volume. On both datasets, training the algorithm on a small data subset and applying it to the full dataset takes a fraction of the time required by the currently used semiautomated protocols. Our software, raw data and ground truth annotations are available at http://hci.iwr.uni-heidelberg.de/Benchmarks/. The development version of the code can be found at https://github.com/RWalecki/ATMA.
Date Issued
2015-06-12
Date Acceptance
2015-04-18
Citation
Journal of Microscopy, 2015, 259 (2), pp.143-154
ISSN
1365-2818
Publisher
Wiley
Start Page
143
End Page
154
Journal / Book Title
Journal of Microscopy
Volume
259
Issue
2
Copyright Statement
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Subjects
Axon
Ranvier
detection
nerve
segmentation
tracing
Algorithms
Animals
Axons
Datasets as Topic
Imaging, Three-Dimensional
Microscopy, Electron
Peripheral Nerves
Ranvier's Nodes
Rats
Supervised Machine Learning
Vagus Nerve
Microscopy
0204 Condensed Matter Physics
0912 Materials Engineering
0601 Biochemistry And Cell Biology
Publication Status
Published