PT  - JOURNAL ARTICLE
AU  - Belevich, Ilya
AU  - Jokitalo, Eija
TI  - DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
AID  - 10.1101/2020.07.13.200105
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.13.200105
4099  - http://biorxiv.org/content/early/2020/07/14/2020.07.13.200105.short
4100  - http://biorxiv.org/content/early/2020/07/14/2020.07.13.200105.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.