Data for McGovern et al, 2024: Finding and Following: A deep learning-based pipeline for tracking platelets during thrombus formation in vivo and ex vivo

Data for McGovern et al, 2024: Finding and Following: A deep learning-based pipeline for tracking platelets during thrombus formation in vivo and ex vivoIn these directories you will find example data to run the software described in the paper:segmentationtraining_data: example frames (training_data/training_images) and corresponding ground truth segmentations (training_data/training_gt) that can be used to train the U-net described in the paper.{exvivo,invivo}_example: example images with multiple matching corresponding manual segmentations that can be used to validate the U-net's performance.tracking image datasets that can be segmented with the U-net trained from the segmentation data, then tracked and analysed.The data format is OME-NGFF v0.4, an emerging open format for bioimaging data and metadata. It can therefore be opened with open software in various ecosystems[1]. To open the files in napari, install the napari-ome-zarr plugin and then (for example):napari --plugin napari-ome-zarr tracking/mouse_invivo/200527_IVMTR73_Inj4_saline_exp3.ome.zarrNote, however, that due to a current implementation issue with napari-ome-zarr, the opened segmentation files will not be manually editable with napari. For the moment, use the data loading widget from iterseg if you want to paint into the segmentation data.https://ngff.openmicroscopy.org/tools/ ↩︎