Data from: Automated cell lineage reconstruction using label-free 4D microscopy ...
Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time-lapse imaging. The embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and genera...
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Dryad
2024
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Online Access: | https://dx.doi.org/10.5061/dryad.zcrjdfnkz https://datadryad.org/stash/dataset/doi:10.5061/dryad.zcrjdfnkz |
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ftdatacite:10.5061/dryad.zcrjdfnkz 2024-09-15T18:28:57+00:00 Data from: Automated cell lineage reconstruction using label-free 4D microscopy ... Waliman, Matthew Johnson, Ryan Natesan, Gunalan Tan, Shiqin Santella, Anthony Hong, Ray Shah, Pavak 2024 https://dx.doi.org/10.5061/dryad.zcrjdfnkz https://datadryad.org/stash/dataset/doi:10.5061/dryad.zcrjdfnkz en eng Dryad https://github.com/shahlab-ucla/embGAN https://dx.doi.org/10.1101/2024.01.20.576449 https://github.com/shahlab-ucla/embGAN Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 FOS Biological sciences Microscopy Deep learning Developmental biology Image analysis dataset Dataset 2024 ftdatacite https://doi.org/10.5061/dryad.zcrjdfnkz10.1101/2024.01.20.576449 2024-08-01T11:02:43Z Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time-lapse imaging. The embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and generalizes well to images acquired in multiple labs on multiple instruments. ... : Images were acquired using an Olympus IX83 inverted frame equipped with a UPLSAPO60xs2 objective, a Visitech iSIM multipoint confocal scanner, ASI MX2000XYZ stage, and a Hamamatsu Orca Fusion camera. The mCherry channel of JIM113 was acquired using 594 nm excitation and a 605 nm long-pass emission filter using 150 ms exposures and a laser power that was empirically tuned to not cause any qualitative developmental delays versus un-imaged control embryos and maintain a ~100% hatch rate for imaged embryos. Embryos were imaged every 60 seconds with a 750 nm z spacing. DIC images were acquired with the Visitech scanner in brightfield bypass mode, a 50 ms camera exposure and the LED light source tuned to not generate any saturated pixels in the image. DIC illumination was generated using an Olympus UCD8 manual condenser equipped with a U525 oil immersion 1.4 NA top lens and a DICTHR tilt-shift slider. Images were acquired using a micro-manager and cropped and converted to individual tiff volumes using Fiji. ... Dataset Orca DataCite |
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language |
English |
topic |
FOS Biological sciences Microscopy Deep learning Developmental biology Image analysis |
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FOS Biological sciences Microscopy Deep learning Developmental biology Image analysis Waliman, Matthew Johnson, Ryan Natesan, Gunalan Tan, Shiqin Santella, Anthony Hong, Ray Shah, Pavak Data from: Automated cell lineage reconstruction using label-free 4D microscopy ... |
topic_facet |
FOS Biological sciences Microscopy Deep learning Developmental biology Image analysis |
description |
Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time-lapse imaging. The embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and generalizes well to images acquired in multiple labs on multiple instruments. ... : Images were acquired using an Olympus IX83 inverted frame equipped with a UPLSAPO60xs2 objective, a Visitech iSIM multipoint confocal scanner, ASI MX2000XYZ stage, and a Hamamatsu Orca Fusion camera. The mCherry channel of JIM113 was acquired using 594 nm excitation and a 605 nm long-pass emission filter using 150 ms exposures and a laser power that was empirically tuned to not cause any qualitative developmental delays versus un-imaged control embryos and maintain a ~100% hatch rate for imaged embryos. Embryos were imaged every 60 seconds with a 750 nm z spacing. DIC images were acquired with the Visitech scanner in brightfield bypass mode, a 50 ms camera exposure and the LED light source tuned to not generate any saturated pixels in the image. DIC illumination was generated using an Olympus UCD8 manual condenser equipped with a U525 oil immersion 1.4 NA top lens and a DICTHR tilt-shift slider. Images were acquired using a micro-manager and cropped and converted to individual tiff volumes using Fiji. ... |
format |
Dataset |
author |
Waliman, Matthew Johnson, Ryan Natesan, Gunalan Tan, Shiqin Santella, Anthony Hong, Ray Shah, Pavak |
author_facet |
Waliman, Matthew Johnson, Ryan Natesan, Gunalan Tan, Shiqin Santella, Anthony Hong, Ray Shah, Pavak |
author_sort |
Waliman, Matthew |
title |
Data from: Automated cell lineage reconstruction using label-free 4D microscopy ... |
title_short |
Data from: Automated cell lineage reconstruction using label-free 4D microscopy ... |
title_full |
Data from: Automated cell lineage reconstruction using label-free 4D microscopy ... |
title_fullStr |
Data from: Automated cell lineage reconstruction using label-free 4D microscopy ... |
title_full_unstemmed |
Data from: Automated cell lineage reconstruction using label-free 4D microscopy ... |
title_sort |
data from: automated cell lineage reconstruction using label-free 4d microscopy ... |
publisher |
Dryad |
publishDate |
2024 |
url |
https://dx.doi.org/10.5061/dryad.zcrjdfnkz https://datadryad.org/stash/dataset/doi:10.5061/dryad.zcrjdfnkz |
genre |
Orca |
genre_facet |
Orca |
op_relation |
https://github.com/shahlab-ucla/embGAN https://dx.doi.org/10.1101/2024.01.20.576449 https://github.com/shahlab-ucla/embGAN |
op_rights |
Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 |
op_doi |
https://doi.org/10.5061/dryad.zcrjdfnkz10.1101/2024.01.20.576449 |
_version_ |
1810470382736506880 |