Deep MIB - segmentation using Deep Learning

The deep learning tool (Deep MIB) provides access to training of deep convolutional networks over the user data and utilization of those networks for image segmentation tasks.

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Contents

Overview

For details of deep learning with DeepMIB please refer to the following tutorials:
DeepMIB: 2D U-net for image segmentation
DeepMIB: 3D U-net for image segmentation
DeepMIB, features and updates in MIB 2.80
DeepMIB, 2D Patch-wise mode


The typical semantic segmentation workflow consists of two parts: During network training users specify type of the network architecture (the Network panel of Deep MIB) and provide images and ground truth models (the Directories and Preprocessing tab). For training, the provided data will be split into two sets: one set to be used for the actual training (normally it contains most of the ground truth data) and another for validation. The network trains itself over the training set, while checking own performance using the validation set (the Training tab).
The pretrained network is saved to disk and can be distributed to predict (the Predict tab) unseen datasets.
Please refer to the documentation below for details of various options available in MIB.

For the list of available workflows and networks jump to description of the Network panel

Network panel

This panel is used to select workflow and convolutional network architecture to be used during training

Details of the Network panel

Directories and Preprocessing tab

This tab allows choosing directories with images for training and prediction as well as various parameters used during image loading and preprocessing.

Details of the Directories and Preprocessing tab

Train tab

This tab contains settings for generating deep convolutional network and training.

Details of the Train tab

Predict tab

The trained networks can be loaded to Deep MIB and used for prediction of new datasets

Details of the Predict tab

Options tab

Some additional options and settings are available in this tab

Details of the Predict tab

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