Published August 8, 2022 | Version v1.0
Dataset Open

Training Data for "DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"

Authors/Creators

  • 1. Imaging Core Facility, Biocenter, University of Würzburg, Würzburg, 97074, Germany
  • 2. Department of Neurology, University of Würzburg, Würzburg, 97074, Germany
  • 3. Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, 97074, Germany

Description

This folder contains the training dataset used for the paper

"DeepCLEM: automated registration for correlative light and electron microscopy using deep learning"

Rick Seifert, Sebastian M. Markert, Sebastian Britz, Veronika Perschin, Christoph Erbacher, Christian Stigloher and Philip Kollmannsberger

F1000Research 9:1275 (2020), https://f1000research.com/articles/9-1275

------------------------------------------------------------

These are 117+4 manually aligned CLEM images of C.elegans acquired by Sebastian M. Markert, Sebastian Britz and Rick Seifert in the Electron Microscopy Facility of the Biocenter of University of Wuerzburg, Germany. For details and experimental protocols, please see the paper linked above.

Contents:

  • "fluo_training": Fluorescence microscopic channel of the 117 training images 
  • "sem_training": Scanning electron microscopic channel of the 117 training images
  • "fluo_validation": Fluorescence microscopic channel of the 4 validation images 
  • "sem_validation": Scanning electron microscopic channel of the 4 validation images

License: CC-BY 4.0

Files

DeepCLEM_training.zip

Files (16.8 MB)

Name Size Download all
md5:0df67bdfff10e6267ed9b3226168353a
16.8 MB Preview Download