DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation

Ilya Belevich; Eija Jokitalo

doi:10.1371/journal.pcbi.1008374

DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation

PLoS Comput Biol. 2021 Mar 2;17(3):e1008374. doi: 10.1371/journal.pcbi.1008374. eCollection 2021 Mar.

Authors

Ilya Belevich¹, Eija Jokitalo¹

Affiliation

¹ Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.

Abstract

We present DeepMIB, a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy datasets on any workstation. We demonstrate its successful application for segmentation of 2D and 3D electron and multicolor light microscopy datasets with isotropic and anisotropic voxels. We distribute DeepMIB as both an open-source multi-platform Matlab code and as compiled standalone application for Windows, MacOS and Linux. It comes in a single package that is simple to install and use as it does not require knowledge of programming. DeepMIB is suitable for everyone interested of bringing a power of deep learning into own image segmentation workflows.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Deep Learning*
Image Processing, Computer-Assisted / methods
Neural Networks, Computer*
Software*
User-Computer Interface

Grants and funding

The research was supported by Biocenter Finland and Academy of Finland (projects 1287975 and 1331998). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.