CNNArt

Automatic and reference-free MR artifact detection

• localization and quantification of artifacts (motion, magnetic field inhomogeneity and noise) in binary or multi-class setting
• correction of motion-induced artifacts (rigid and non-rigid motion)

Visualization of trained network architectures

• visualize the trained kernels and feature maps
• deep visualization: significance map of trained network content, backpropagate most-likely input patch and sparse attractor points of a test image

GUI

easy-to-use graphical interface for medical deep learning

• 2D/3D data viewer
• data preprocessing: labeling, patching, data augmentation, data splitting
• network training: parameter setting, training/validation/test set selection, call to DL backend (keras, Tensorflow, ...)
• test data evaluation: accuracy/loss plots, confusion matrix and derived metrics
• network visualization: kernel weights, feature maps and deep visualization

Usage

Install the requirements

$ python3 -m pip install -r requirements.txt

direct

1. define database layout in config/database/_NAME_OF_DATABASE_.csv (as specified in param.yml -> MRdatabase)
2. edit parameters in config/param.yml
3. run code via main.py

GUI

training/prediction can also be invoked from the GUI. Please adapt mainGUI_Template.py according to your needs
Qt_main.py

calling structure

main.py ==> model.fTrain()/fPredict()

Networks

References

1. Küstner, T., Liebgott, A., Mauch, L., Martirosian, P., Bamberg, F., Nikolaou, K., Yang B., Schick F. & Gatidis, S. (2017). Automated reference-free detection of motion artifacts in magnetic resonance images. Magnetic Resonance Materials in Physics, Biology and Medicine, 1-14.
   
2. Küstner, T., Jandt, M., Liebgott, A., Mauch, L., Martirosian, P., Bamberg, F., Nikolaou, K., Gatidis, S., Schick, F. & Yang, B. (2018). Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
   
3. Küstner, T., Liu, K., Liebgott, A., Mauch, L., Martirosian, P., Bamberg, F., Nikolaou, K., Yang, B., Schick, F. & Gatidis, S. (2018). Simultaneous detection and identification of MR artifact types in whole-body imaging. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM).
   
4. Küstner, T., Jandt, M., Liebgott, A., Mauch, L., Martirosian, P., Bamberg, F., Nikolaou, K., Gatidis, S., Yang, B. & Schick, F. (2018). Motion artifact quantification and localization for whole-body MRI. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM).
   
5. Liebgott, A., Milde, S., Jandt, M., Mauch, L., Martirosian, P., Bamberg, F., Schick, F., Nikolaou, K., Yang, B., Gatidis, S. & Küstner, T. (2018). Impact of Labeling Process on Automated Motion Artifact Detection in Whole-Body MR Images with a Deep Learning Approach: A Comparative Study. Proceedings of the ISMRM Workshop on Machine Learning.
   
6. Küstner, T., Liegbott, A., Mauch, L., Martirosian, P., Schick, F., Bamberg, F., Nikolaou, K., Yang, B. & Gatidisi, S. (2017). Automatic reference-free motion artifact detection and quantification in T1-weighted MR images of the head and abdomen. Proceedings of the Annual Scientific Meeting (ESMRMB).
   
7. Küstner, T., Liebgott, A., Mauch, L., Martirosian, P., Nikolaou, K., Schick, F., Yang, B. & Gatidis, S. (2017). Automatic reference-free detection and quantification of MR image artifacts in human examinations due to motion. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM).