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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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

The typical 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.

Network panel

The upper part of Deep MIB is occupied with the Network panel. This panel is used to select one of the available architectures.
Always start a new project with selection of the architecture:

The Network filename button allows to choose a file for saving the network or for loading the pretrained network from a disk.
This button is only available when either the Train or Predict tab is selected. When the Train tab is selected, press of the this button defines a file for saving the network. When the Predict tab is selected, a user can choose a file with the pretrained network to be used for prediction. For ease of navigation the button is color-coded to match the active tab.

Directories and Preprocessing tab

This tab allows choosing directories with images and specifying certain preprocessing parameters.

When all directories are defined press the Preprocess button to start.

Train tab

This tab contains settings for assembling the network and training. Before processing further please finish the preprocessing part, see above.

Before starting the training process it is important to check and if needed modify the settings. Also, use the Network filename button in the Network panel to select filename for the network.

To start training press the Train button highlighted under the panel. If a network already existing under the provided Network filename it is possible to continue training from that point. Upon training a plot with accuracy and loss is shown; it is possible to stop training at any moment by pressing the stop button at the right upper side of the window (the training windows are different in the compiled and Matlab versions of MIB)

After the training, the network is saved to a file specified in the Network filename editbox of the Network panel.

Predict tab

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

To start with prediction:

Additional options:

Options tab

Some additional options and settings are available in this tab

Config files panel

This panel brings access to loading or saving Deep MIB config files.
The config files contain all settings of Deep MIB including the network name and input and output directories but excluding the actual trained network. Normally, these files are automatically created during the training process and stored next to the network *.mibDeep files also in Matlab format using the *.mibCfg extension.
Alternatively, the files can be saved manually by pressing the Save button.

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