Success classification for deformable medical image registration outcomes

This codebase tries to learn what images are successfully registered to a target image following deformable image registration. Our implementation currently is specific to medical images, in particular to CT images. This is a work in progress.

ct_classifier.py: Defines the dataloader and the neural network ct_classifier_train.py: Trains the network on a specified number of training data and saves the trained model ct_classifier_test.py: Tests the network on a left out dataset

Dependencies:

• Torch: pip install torchvision
• NiBabel: pip install nibabel
• SciPy pip install scipy
• Matplotlib pip install matplotlib

Run:

• Clone this repository
• If no training data, run ct_classifier_test.py. This will use our pretrained network to test on your data
• If training data is available, first run ct_classifier_train.py. This will train on the given training data and save the trained model. Next run ct_classifier_test.py using the new trained model.