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.
Highlights
During recent years, improved availability of high-performance computing resources, and especially graphics processing units (GPUs), has boosted applications of deep learning techniques into many aspects of our lives
In most cases, deep learning is still considered as a complex task that only image analysis experts can master
With DeepMIB we address this problem and provide the community with a user-friendly and open-source tool to train convolutional neural networks and apply them to segment 2D and 3D grayscale or multi-color datasets
Summary
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. In most cases, deep learning is still considered as a complex task that only image analysis experts can master. With DeepMIB we address this problem and provide the community with a user-friendly and open-source tool to train convolutional neural networks and apply them to segment 2D and 3D grayscale or multi-color datasets. This is a PLOS Computational Biology Software paper
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
