cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13377960
title: HUVEC CD44 siRNA perfusion tracking dataset

description: This dataset contains tracking results of AsPC1 and MiaPaca cells perfused on CD44 siRNA-silenced endothelial monolayers under physiological flow speeds.

Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.

Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. 

The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). 

Specifications





Sample information






AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). 




CD44 siRNA silencing of the HUVEC monolayer





Imaging specs






Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images (16-bit)




Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)




Recording speed 25 frames/s





DL models:






Cancer cells: https://doi.org/10.5281/zenodo.10572122 




Neutrophils: https://doi.org/10.5281/zenodo.10572231




Mononucleated cells: https://doi.org/10.5281/zenodo.10572200




Model Training and predictions: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Tracking parameters (TrackMate):






Detection: label detector




Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. 




Track filtering: min number of spots in the tracks 11.79 





Tracking analysis






Tracks were analyzed using a customized CellTracksColab notebook (https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab)




Contents of the repository





Analysis.zip




As_HUsi1.zip dataset




As_HUsi2.zip dataset




As_HUsi3.zip dataset




As_HUsiCtrl.zip dataset




Mia_HUsi1.zip dataset




Mia_HUsi2.zip dataset




Mia_HUsi3.zip dataset




Mia_HUsiCtrl.zip dataset





Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.13377961
title: HUVEC CD44 siRNA perfusion tracking dataset

description: This dataset contains tracking results of AsPC1 and MiaPaca cells perfused on CD44 siRNA-silenced endothelial monolayers under physiological flow speeds.

Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.

Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. 

The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). 

Specifications





Sample information






AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). 




CD44 siRNA silencing of the HUVEC monolayer





Imaging specs






Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images (16-bit)




Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)




Recording speed 25 frames/s





DL models:






Cancer cells: https://doi.org/10.5281/zenodo.10572122 




Neutrophils: https://doi.org/10.5281/zenodo.10572231




Mononucleated cells: https://doi.org/10.5281/zenodo.10572200




Model Training and predictions: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Tracking parameters (TrackMate):






Detection: label detector




Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. 




Track filtering: min number of spots in the tracks 11.79 





Tracking analysis






Tracks were analyzed using a customized CellTracksColab notebook (https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab)




Contents of the repository





Analysis.zip




As_HUsi1.zip dataset




As_HUsi2.zip dataset




As_HUsi3.zip dataset




As_HUsiCtrl.zip dataset




Mia_HUsi1.zip dataset




Mia_HUsi2.zip dataset




Mia_HUsi3.zip dataset




Mia_HUsiCtrl.zip dataset





Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.13379626
title: PDAC cells CD44 siRNA perfusion tracking dataset

description: This dataset contains tracking results of CD44 siRNA-silenced AsPC1, and MiaPaca cells perfused on endothelial monolayers under physiological flow speeds.

Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.

Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate. Tracking results were analyzed using a custom CellTracksColab notebook. 

The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip).  

Specifications





Sample information






AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). 




CD44 siRNA silencing of PDACs prior to perfusion





Imaging specs






Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images (16-bit)




Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)




Recording speed 25 frames/s





DL models:






Cancer cells: https://doi.org/10.5281/zenodo.10572122 




Neutrophils: https://doi.org/10.5281/zenodo.10572231




Mononucleated cells: https://doi.org/10.5281/zenodo.10572200




Model Training and predictions: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Tracking parameters (TrackMate):






Detection: label detector




Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. 




Track filtering: min number of spots in the tracks 11.79 





Tracking analysis






Tracks were analyzed using a customized CellTracksColab notebook (https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab)




 Contents of the repository





Analysis.zip




As_TCsi1.zip dataset




As_TCsi2.zip dataset




As_TCsi3.zip dataset




As_TCsiCtrl.zip dataset




Mia_TCsi1.zip dataset




Mia_TCsi2.zip dataset




Mia_TCsi3.zip dataset




Mia_TCsiCtrl.zip dataset





Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.13379627
title: PDAC cells CD44 siRNA perfusion tracking dataset

description: This dataset contains tracking results of CD44 siRNA-silenced AsPC1, and MiaPaca cells perfused on endothelial monolayers under physiological flow speeds.

Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.

Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate. Tracking results were analyzed using a custom CellTracksColab notebook. 

The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip).  

Specifications





Sample information






AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). 




CD44 siRNA silencing of PDACs prior to perfusion





Imaging specs






Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images (16-bit)




Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)




Recording speed 25 frames/s





DL models:






Cancer cells: https://doi.org/10.5281/zenodo.10572122 




Neutrophils: https://doi.org/10.5281/zenodo.10572231




Mononucleated cells: https://doi.org/10.5281/zenodo.10572200




Model Training and predictions: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Tracking parameters (TrackMate):






Detection: label detector




Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. 




Track filtering: min number of spots in the tracks 11.79 





Tracking analysis






Tracks were analyzed using a customized CellTracksColab notebook (https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab)




 Contents of the repository





Analysis.zip




As_TCsi1.zip dataset




As_TCsi2.zip dataset




As_TCsi3.zip dataset




As_TCsiCtrl.zip dataset




Mia_TCsi1.zip dataset




Mia_TCsi2.zip dataset




Mia_TCsi3.zip dataset




Mia_TCsiCtrl.zip dataset





Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.13584214
title: CD44 Blocking Antibody perfusion tracking dataset

description: This dataset contains tracking results of different combinations of CD44 antibody-blocked AsPC1 and MiaPaca cells perfused on CD44 antibody-blocked endothelial monolayers under physiological flow speeds.

Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.

Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. 

The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). 

 Specifications





Sample information






AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). 




CD44 antibody blocking of PDACs, HUVECs, or both prior to perfusion





Imaging specs






Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images (16-bit)




Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)




Recording speed 25 frames/s





DL models:






Cancer cells: https://doi.org/10.5281/zenodo.10572122 




Neutrophils: https://doi.org/10.5281/zenodo.10572231




Mononucleated cells: https://doi.org/10.5281/zenodo.10572200




Model Training and predictions: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Tracking parameters (TrackMate):






Detection: label detector




Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. 




Track filtering: min number of spots in the tracks 11.79 





Tracking analysis






Tracks were analyzed using a customized CellTracksColab notebook (https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab)




Contents of the repository





Analysis_AsPC1.zip dataset




Analysis_Miapaca.zip dataset




As_blockboth.zip dataset




As_ctrlblock.zip dataset




As_HUblock.zip dataset




As_TCblock.zip dataset




Mia_blockboth.zip




Mia_ctrlblock.zip




Mia_HUblock.zip




Mia_TCblock.zip



 



Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.13584215
title: CD44 Blocking Antibody perfusion tracking dataset

description: This dataset contains tracking results of different combinations of CD44 antibody-blocked AsPC1 and MiaPaca cells perfused on CD44 antibody-blocked endothelial monolayers under physiological flow speeds.

Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.

Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. 

The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). 

 Specifications





Sample information






AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). 




CD44 antibody blocking of PDACs, HUVECs, or both prior to perfusion





Imaging specs






Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images (16-bit)




Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)




Recording speed 25 frames/s





DL models:






Cancer cells: https://doi.org/10.5281/zenodo.10572122 




Neutrophils: https://doi.org/10.5281/zenodo.10572231




Mononucleated cells: https://doi.org/10.5281/zenodo.10572200




Model Training and predictions: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Tracking parameters (TrackMate):






Detection: label detector




Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. 




Track filtering: min number of spots in the tracks 11.79 





Tracking analysis






Tracks were analyzed using a customized CellTracksColab notebook (https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab)




Contents of the repository





Analysis_AsPC1.zip dataset




Analysis_Miapaca.zip dataset




As_blockboth.zip dataset




As_ctrlblock.zip dataset




As_HUblock.zip dataset




As_TCblock.zip dataset




Mia_blockboth.zip




Mia_ctrlblock.zip




Mia_HUblock.zip




Mia_TCblock.zip



 



Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.13627036
title: Hyaluronidase treatment perfusion tracking dataset

description: This dataset contains tracking results of different combinations of Hyaluronidase-treated AsPC1 and MiaPaca cells perfused on Hyaluronidase-treated endothelial monolayer under physiological flow speeds.

Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.

Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. 

The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). 

 Spesifications





Sample information






AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). 




Hyaluronidase treatment PDACs or HUVECs prior to perfusion





Imaging specs






Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images (16-bit)




Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)




Recording speed 25 frames/s





DL models:






Cancer cells: https://doi.org/10.5281/zenodo.10572122 




Neutrophils: https://doi.org/10.5281/zenodo.10572231




Mononucleated cells: https://doi.org/10.5281/zenodo.10572200




Model Training and predictions: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Tracking parameters (TrackMate):






Detection: label detector




Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. 




Track filtering: min number of spots in the tracks 11.79 





Tracking analysis






Tracks were analyzed using a customized CellTracksColab notebook (https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab)




Contents of the repository





Analysis.zip dataset




As_ctrl.zip




As_HUdigestion.zip




As_TCdigestion.zip




Mia_ctrl.zip




Mia_HUdig.zip




Mia_TCdig.zip



 



Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.5281/zenodo.13627037
title: Hyaluronidase treatment perfusion tracking dataset

description: This dataset contains tracking results of different combinations of Hyaluronidase-treated AsPC1 and MiaPaca cells perfused on Hyaluronidase-treated endothelial monolayer under physiological flow speeds.

Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.

Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. 

The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). 

 Spesifications





Sample information






AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). 




Hyaluronidase treatment PDACs or HUVECs prior to perfusion





Imaging specs






Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images (16-bit)




Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)




Recording speed 25 frames/s





DL models:






Cancer cells: https://doi.org/10.5281/zenodo.10572122 




Neutrophils: https://doi.org/10.5281/zenodo.10572231




Mononucleated cells: https://doi.org/10.5281/zenodo.10572200




Model Training and predictions: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)





Tracking parameters (TrackMate):






Detection: label detector




Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. 




Track filtering: min number of spots in the tracks 11.79 





Tracking analysis






Tracks were analyzed using a customized CellTracksColab notebook (https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab)




Contents of the repository





Analysis.zip dataset




As_ctrl.zip




As_HUdigestion.zip




As_TCdigestion.zip




Mia_ctrl.zip




Mia_HUdig.zip




Mia_TCdig.zip



 



Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
DOI: 10.15468/dl.bmwknh
title: Occurrence Download

description: A dataset containing 304 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cynodon plectostachyus (K.Schum.) Pilg.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 304 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009802-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10572121
title: StarDist_BF_cancer_cell_dataset_20x

description: This repository contains a StarDist deep learning model and its training and validation datasets designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 20x magnification. Using computational methods, the initial dataset of 20 manually annotated images was augmented to 160 paired images. The model was trained over 400 epochs and achieved an average F1 Score of 0.921, demonstrating high accuracy in cell segmentation tasks.

Specifications





Model: StarDist for cancer cell segmentation on endothelial cells (20x magnification)




Training Dataset:






Number of Original Images: 20 paired brightfield microscopy images and label masks




Microscope: Nikon Eclipse Ti2-E, 20x objective




Data Type: Brightfield microscopy images with manually segmented masks




File Format: TIFF (.tif)






Brightfield Images: 16-bit




Masks: 8-bit





Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)





Training Parameters:






Epochs: 400




Patch Size: 992 x 992 pixels




Batch Size: 2





Performance:






Average F1 Score: 0.921




Average IoU: 0.793





Model Training: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki)



 



Reference

Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers


Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet

bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654