Software for non‐parametric image registration of 2‐photon imaging data
Functional 2‐photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non‐rigid movement artifacts which requires high‐accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with...
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| Published in: | Journal of biophotonics Vol. 15; no. 8; pp. e202100330 - n/a |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Weinheim
WILEY‐VCH Verlag GmbH & Co. KGaA
01.08.2022
Wiley Subscription Services, Inc |
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| ISSN: | 1864-063X, 1864-0648, 1864-0648 |
| Online Access: | Get full text |
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| Abstract | Functional 2‐photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non‐rigid movement artifacts which requires high‐accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with established performance in many computer vision areas. However, it has yet to be adapted to the statistics of 2‐photon neuroimaging data. In this work, we present the motion compensation method Flow‐Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal‐to‐noise ratio 2‐photon imaging data and is able to compensate high‐divergence displacements during local drug injections. The method is based on statistics of such data and integrates previous advances in variational OF estimation. Our method is available as an easy‐to‐use ImageJ/FIJI plugin as well as a MATLAB toolbox with modular, object oriented file IO, native multi‐channel support and compatibility with existing 2‐photon imaging suites.
We present the non‐parametric, high‐accuracy and fast motion compensation method Flow‐Registration that can compensate challenging 2‐Photon neuroimaging videos which are contaminated with large and or high divergence displacements. Our method is publicly available as a MATLAB toolbox and ImageJ/FIJI plugin on GitHub: https://github.com/phflot/flow_registration |
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| AbstractList | Functional 2‐photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non‐rigid movement artifacts which requires high‐accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with established performance in many computer vision areas. However, it has yet to be adapted to the statistics of 2‐photon neuroimaging data. In this work, we present the motion compensation method Flow‐Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal‐to‐noise ratio 2‐photon imaging data and is able to compensate high‐divergence displacements during local drug injections. The method is based on statistics of such data and integrates previous advances in variational OF estimation. Our method is available as an easy‐to‐use ImageJ/FIJI plugin as well as a MATLAB toolbox with modular, object oriented file IO, native multi‐channel support and compatibility with existing 2‐photon imaging suites.
We present the non‐parametric, high‐accuracy and fast motion compensation method Flow‐Registration that can compensate challenging 2‐Photon neuroimaging videos which are contaminated with large and or high divergence displacements. Our method is publicly available as a MATLAB toolbox and ImageJ/FIJI plugin on GitHub: https://github.com/phflot/flow_registration Functional 2-photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non-rigid movement artifacts which requires high-accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with established performance in many computer vision areas. However, it has yet to be adapted to the statistics of 2-photon neuroimaging data. In this work, we present the motion compensation method Flow-Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal-to-noise ratio 2-photon imaging data and is able to compensate high-divergence displacements during local drug injections. The method is based on statistics of such data and integrates previous advances in variational OF estimation. Our method is available as an easy-to-use ImageJ/FIJI plugin as well as a MATLAB toolbox with modular, object oriented file IO, native multi-channel support and compatibility with existing 2-photon imaging suites.Functional 2-photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non-rigid movement artifacts which requires high-accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with established performance in many computer vision areas. However, it has yet to be adapted to the statistics of 2-photon neuroimaging data. In this work, we present the motion compensation method Flow-Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal-to-noise ratio 2-photon imaging data and is able to compensate high-divergence displacements during local drug injections. The method is based on statistics of such data and integrates previous advances in variational OF estimation. Our method is available as an easy-to-use ImageJ/FIJI plugin as well as a MATLAB toolbox with modular, object oriented file IO, native multi-channel support and compatibility with existing 2-photon imaging suites. Functional 2‐photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non‐rigid movement artifacts which requires high‐accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with established performance in many computer vision areas. However, it has yet to be adapted to the statistics of 2‐photon neuroimaging data. In this work, we present the motion compensation method Flow‐Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal‐to‐noise ratio 2‐photon imaging data and is able to compensate high‐divergence displacements during local drug injections. The method is based on statistics of such data and integrates previous advances in variational OF estimation. Our method is available as an easy‐to‐use ImageJ/FIJI plugin as well as a MATLAB toolbox with modular, object oriented file IO, native multi‐channel support and compatibility with existing 2‐photon imaging suites. Functional 2‐photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non‐rigid movement artifacts which requires high‐accuracy movement correction. Variational optical flow (OF) estimation is a group of methods for motion analysis with established performance in many computer vision areas. However, it has yet to be adapted to the statistics of 2‐photon neuroimaging data. In this work, we present the motion compensation method Flow‐Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal‐to‐noise ratio 2‐photon imaging data and is able to compensate high‐divergence displacements during local drug injections. The method is based on statistics of such data and integrates previous advances in variational OF estimation. Our method is available as an easy‐to‐use ImageJ/FIJI plugin as well as a MATLAB toolbox with modular, object oriented file IO, native multi‐channel support and compatibility with existing 2‐photon imaging suites. |
| Author | Flotho, Philipp Strauss, Daniel J. Nomura, Shinobu Kuhn, Bernd |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35289100$$D View this record in MEDLINE/PubMed |
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| Snippet | Functional 2‐photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non‐rigid movement... Functional 2-photon microscopy is a key technology for imaging neuronal activity. The recorded image sequences, however, can contain non-rigid movement... |
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| SubjectTerms | Computer vision confocal microscopy Divergence Image registration ImageJ/FIJI plugin MATLAB toolbox Medical imaging Motion compensation movement correction Neuroimaging Nonparametric statistics optical flow Optical flow (image analysis) optical imaging Photons Statistical analysis two‐photon microscopy |
| Title | Software for non‐parametric image registration of 2‐photon imaging data |
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