DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
Abstract
Deep learning approaches are highly sought after solutions for coping with large amounts of collected datasets and are expected to become an essential part of imaging workflows. However, in most cases, deep learning is still considered as a complex task that only image analysis experts can master. DeepMIB addresses this problem and provides the community with a user-friendly and open-source tool to train convolutional neural networks and apply them to segment 2D and 3D light and electron microscopy datasets.
02 /Specification & Classification
Click any tag to browse matching papersReproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- CDeep3M - Plug-and-Play cloud based deep learning for image segmentation of light, electron and X-ray microscopy
M. Haberl, C. Churas, … M. H. Ellisman·bioRxiv·Jun 21, 2018
- DeepImageJ: A user-friendly environment to run deep learning models in ImageJ
E. Gomez-de-Mariscal, C. Garcia-Lopez-de-Haro, … D. Sage·bioRxiv·May 6, 2021
- Piximi - An Images to Discovery web tool for bioimages and beyond
Levin M Moser, Nodar Gogoberidze, … Beth A. Cimini·bioRxiv·Jun 3, 2024
- ZeroCostDL4Mic: an open platform to use Deep-Learning in Microscopy
Lucas von Chamier, Romain F. Laine, … Ricardo Henriques·Nature CommunicationsPeer-reviewed·Apr 15, 2021
- MIA: An Open Source Standalone Deep Learning Application for Microscopic Image Analysis
Nils Körber·Cell Reports MethodsPeer-reviewed·Jun 26, 2023
- CDeep3M-Preview: Online segmentation using the deep neural network model zoo
Matthias Georg Haberl, Willy Wong, … Mark H. Ellisman·bioRxiv·Mar 26, 2020