Registration Using Recurrent Models

Combining Unet with LSTMs to register 2D slices in a 3D image.

Installation:

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Start by cloning this repositiory:

git clone https://github.com/Armin-Saadat/bachelor-thesis.git
cd bachelor-thesis

And install the dependencies:

pip install ./source-code/pystrum
pip install ./source-code/neurite
pip install ./source-code/voxelmorph

Train:

Available Models:

• 2d.py: classic 2d U-Net
• fc_bottleneck.py: 2d U-Net with Fully-Connected LSTM in the lowest layer
• conv_all_layers.py: 2d U-Net with Convolutional LSTMs in all layers

python3.7 train-scripts/<file-name>

Evaluate:

Available Models:

• 2d_eval.py: classic 2d U-Net
• fc_bottleneck_eval.py: 2d U-Net with Fully-Connected LSTM in the lowest layer
• conv_all_layers_eval.py: 2d U-Net with Convolutional LSTMs in all layers

python3.7 eval-scripts/<file-name>

In each file, there is an argument section named Args. Using Args, you can set the hyper-parameters of the model and determine the path for saving and loading the trained models.