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

<p><strong>This folder contains the training dataset used for the paper</strong></p> <p>&quot;DeepCLEM: automated registration for correlative light and electron microscopy using deep learning&quot;</p> <p><em>Rick Seifert, Sebastian M. Markert, Sebastian Britz, Veronika Perschin, Christoph Erbacher, Christian Stigloher and Philip Kollmannsberger</em></p> <p>F1000Research 9:1275 (2020), https://f1000research.com/articles/9-1275</p> <p>------------------------------------------------------------</p> <p>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.</p> <p>Contents:</p> <ul> <li>&quot;fluo_training&quot;: Fluorescence microscopic channel of the 117 training images&nbsp;</li> <li>&quot;sem_training&quot;: Scanning electron microscopic channel of the 117 training images</li> <li>&quot;fluo_validation&quot;: Fluorescence microscopic channel of the 4 validation images&nbsp;</li> <li>&quot;sem_validation&quot;: Scanning electron microscopic channel of the 4 validation images</li> </ul> <p>License: CC-BY 4.0</p>