I am glad to announce a new release of MIB (version 2.70) that features DeepMIB - a new tool for automatic image segmentation using convolutional neural network.
With DeepMIB you can train 2D (Unet, SegNet) and 3D (Unets for isotropic and anisotropic voxels) CNNs for segmentation of light or electron microscopy datasets.
To use it, download the recent version of MIB for Matlab or standalone (if you do not have Matlab) and start it from Menu->Tools->Deep learning segmentation
2D/3D EM/LM examples:
2D EM dataset: Segmentation of membranes (Serial section Transmission Electron Microscopy dataset of the Drosophila first instar larva ventral nerve cord)
2D LM dataset: Segmentation of nuclei (blue), their boundaries (yellow) and interfaces between adjacent nuclei (red) for random images from a high-throughput screen on human cultured osteocarcinoma U2OS cells (BBBC022 dataset, Broad BioImage Benchmark Collection)
3D EM dataset: Segmentation of mitochondria from the full focused ion beam scanning electron microscopy dataset of the CA1 hippocampus region.
3D LM dataset: Segmentation of inner hair cells and ribbon synapses from mouse inner ear cochlea
A manuscript describing DeepMIB is available from bioRxiv. It is supplemented with datasets, configs and networks used to generate the examples from above.
How to use
- DeepMIB: How to train 2D U-Net for microscopy images (49 minutes)
- DeepMIB: How to train 3D U-Net for microscopy images (34 minutes)
- Added DeepMIB for training and prediction of datasets using deep convolutional networks (
Menu->Tools->Deep learning segmentation)
- Added 2D Elastic Distortion filter (
- Added resizing of the Image Arithmetics window
- Added selection of a seed for random generator for Rename and Shuffle tool
- Fixed issues with importing of chopped cropped datasets