Open AccessDeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
Author(s) -
Ilya Belevich,
Eija Jokitalo
Publication year - 2021
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1008374
Subject(s) - computer science , workflow , convolutional neural network , segmentation , software , deep learning , artificial intelligence , artificial neural network , workstation , source code , image segmentation , code (set theory) , computer graphics (images) , computer vision , pattern recognition (psychology) , computational science , operating system , programming language , database , set (abstract data type)
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.