Tools in development to support analysis of Diffusion-Weighted Imaging (DWI) data, with focus on prostate cancer.
This software is being developed as part of a research project at the Magnetic Resonance Imaging Research Center, Turku, Finland.
Note: This is very much under construction. The program code is written on demand basis, for custom needs at in-house projects.
- Read input data as DICOM, or in custom ASCII or MATLAB formats
- Perform model fitting (Monoexponential ADC, Kurtosis, Stretched exponential, Biexponential)
- Calculate correlation with Gleason score
- Calculate and compare diagnostic ROC AUCs
- Calculate reproducibility measures
- Plotting schemes
- Viewer for multi-slice, multi-b-value DWI DICOM files (uses the Matplotlib GUI widget)
- Build tool for automated pipelining of data processing tasks
- Improved documentation
- Improved build tool
- Autonomous tumor delineation/ROI placement
- Regression classification
- Python 2.7
- pathlib2
- NumPy
- SciPy
- Scikit-Learn
- leastsqbound-scipy (fitting)
- Matplotlib (plotting)
- Pydicom (reading DICOM files)
- h5py (handling HDF5 files)
- scikit-image (texture analysis)
- Mahotas (texture analysis)
- DoIt (task management)