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Automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images
Computational studies can be used to support the development of peripheral nerve interfaces, but currently use simplified models of nerve anatomy, which may impact the applicability of simulation results. To better quantify and model neural anatomy across the population, we have developed an algorit...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224505/ https://www.ncbi.nlm.nih.gov/pubmed/32407341 http://dx.doi.org/10.1371/journal.pone.0233028 |
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author | Tovbis, Daniel Agur, Anne Mogk, Jeremy P. M. Zariffa, José |
author_facet | Tovbis, Daniel Agur, Anne Mogk, Jeremy P. M. Zariffa, José |
author_sort | Tovbis, Daniel |
collection | PubMed |
description | Computational studies can be used to support the development of peripheral nerve interfaces, but currently use simplified models of nerve anatomy, which may impact the applicability of simulation results. To better quantify and model neural anatomy across the population, we have developed an algorithm to automatically reconstruct accurate peripheral nerve models from histological cross-sections. We acquired serial median nerve cross-sections from human cadaveric samples, staining one set with hematoxylin and eosin (H&E) and the other using immunohistochemistry (IHC) with anti-neurofilament antibody. We developed a four-step processing pipeline involving registration, fascicle detection, segmentation, and reconstruction. We compared the output of each step to manual ground truths, and additionally compared the final models to commonly used extrusions, via intersection-over-union (IOU). Fascicle detection and segmentation required the use of a neural network and active contours in H&E-stained images, but only simple image processing methods for IHC-stained images. Reconstruction achieved an IOU of 0.42±0.07 for H&E and 0.37±0.16 for IHC images, with errors partially attributable to global misalignment at the registration step, rather than poor reconstruction. This work provides a quantitative baseline for fully automatic construction of peripheral nerve models. Our models provided fascicular shape and branching information that would be lost via extrusion. |
format | Online Article Text |
id | pubmed-7224505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-72245052020-06-01 Automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images Tovbis, Daniel Agur, Anne Mogk, Jeremy P. M. Zariffa, José PLoS One Research Article Computational studies can be used to support the development of peripheral nerve interfaces, but currently use simplified models of nerve anatomy, which may impact the applicability of simulation results. To better quantify and model neural anatomy across the population, we have developed an algorithm to automatically reconstruct accurate peripheral nerve models from histological cross-sections. We acquired serial median nerve cross-sections from human cadaveric samples, staining one set with hematoxylin and eosin (H&E) and the other using immunohistochemistry (IHC) with anti-neurofilament antibody. We developed a four-step processing pipeline involving registration, fascicle detection, segmentation, and reconstruction. We compared the output of each step to manual ground truths, and additionally compared the final models to commonly used extrusions, via intersection-over-union (IOU). Fascicle detection and segmentation required the use of a neural network and active contours in H&E-stained images, but only simple image processing methods for IHC-stained images. Reconstruction achieved an IOU of 0.42±0.07 for H&E and 0.37±0.16 for IHC images, with errors partially attributable to global misalignment at the registration step, rather than poor reconstruction. This work provides a quantitative baseline for fully automatic construction of peripheral nerve models. Our models provided fascicular shape and branching information that would be lost via extrusion. Public Library of Science 2020-05-14 /pmc/articles/PMC7224505/ /pubmed/32407341 http://dx.doi.org/10.1371/journal.pone.0233028 Text en © 2020 Tovbis et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Tovbis, Daniel Agur, Anne Mogk, Jeremy P. M. Zariffa, José Automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images |
title | Automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images |
title_full | Automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images |
title_fullStr | Automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images |
title_full_unstemmed | Automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images |
title_short | Automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images |
title_sort | automatic three-dimensional reconstruction of fascicles in peripheral nerves from histological images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224505/ https://www.ncbi.nlm.nih.gov/pubmed/32407341 http://dx.doi.org/10.1371/journal.pone.0233028 |
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