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Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes
Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically dete...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6155135/ https://www.ncbi.nlm.nih.gov/pubmed/30250218 http://dx.doi.org/10.1038/s41598-018-32628-3 |
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author | Shahbazi, Ali Kinnison, Jeffery Vescovi, Rafael Du, Ming Hill, Robert Joesch, Maximilian Takeno, Marc Zeng, Hongkui da Costa, Nuno Maçarico Grutzendler, Jaime Kasthuri, Narayanan Scheirer, Walter J. |
author_facet | Shahbazi, Ali Kinnison, Jeffery Vescovi, Rafael Du, Ming Hill, Robert Joesch, Maximilian Takeno, Marc Zeng, Hongkui da Costa, Nuno Maçarico Grutzendler, Jaime Kasthuri, Narayanan Scheirer, Walter J. |
author_sort | Shahbazi, Ali |
collection | PubMed |
description | Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively. |
format | Online Article Text |
id | pubmed-6155135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61551352018-09-28 Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes Shahbazi, Ali Kinnison, Jeffery Vescovi, Rafael Du, Ming Hill, Robert Joesch, Maximilian Takeno, Marc Zeng, Hongkui da Costa, Nuno Maçarico Grutzendler, Jaime Kasthuri, Narayanan Scheirer, Walter J. Sci Rep Article Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively. Nature Publishing Group UK 2018-09-24 /pmc/articles/PMC6155135/ /pubmed/30250218 http://dx.doi.org/10.1038/s41598-018-32628-3 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Shahbazi, Ali Kinnison, Jeffery Vescovi, Rafael Du, Ming Hill, Robert Joesch, Maximilian Takeno, Marc Zeng, Hongkui da Costa, Nuno Maçarico Grutzendler, Jaime Kasthuri, Narayanan Scheirer, Walter J. Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes |
title | Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes |
title_full | Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes |
title_fullStr | Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes |
title_full_unstemmed | Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes |
title_short | Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes |
title_sort | flexible learning-free segmentation and reconstruction of neural volumes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6155135/ https://www.ncbi.nlm.nih.gov/pubmed/30250218 http://dx.doi.org/10.1038/s41598-018-32628-3 |
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