PT  - JOURNAL ARTICLE
AU  - Dennis Segebarth
AU  - Matthias Griebel
AU  - Alexander Dürr
AU  - Cora R. von Collenberg
AU  - Corinna Martin
AU  - Dominik Fiedler
AU  - Lucas B. Comeras
AU  - Anupam Sah
AU  - Nikolai Stein
AU  - Rohini Gupta
AU  - Manju Sasi
AU  - Maren D. Lange
AU  - Ramon O. Tasan
AU  - Nicolas Singewald
AU  - Hans-Christian Pape
AU  - Michael Sendtner
AU  - Christoph M. Flath
AU  - Robert Blum
TI  - DeepFLaSH, a deep learning pipeline for segmentation of fluorescent labels in microscopy images
AID  - 10.1101/473199
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 473199
4099  - http://biorxiv.org/content/early/2018/11/19/473199.short
4100  - http://biorxiv.org/content/early/2018/11/19/473199.full
AB  - Here we present and evaluate DeepFLaSH, a unique deep learning pipeline to automatize the segmentation of fluorescent labels in microscopy images. The pipeline allows training and validation of label-specific convolutional neural network (CNN) models that can be uploaded to an open-source CNN-model library. As there is no ground truth for fluorescent signal segmentation tasks, we evaluated the CNN with respect to inter-coding reliability. Similarity analysis showed that CNN-predictions highly correlated with segmentations by human experts. DeepFLaSH also allows adaptation of pretrained, label-specific CNN-models from our CNN-model library to new datasets by means of transfer learning. We show consistent model-performance on datasets from three independent laboratories after transfer learning, thus ensuring its objectivity and reproducibility. DeepFLaSH runs as a guided, hassle-free open-source tool on a cloud-based virtual notebook with free access to high computing power and requires no machine learning expertise.