Research tools for MRI-based CAD of cancer
Project description
NEW: Switched to Python version 3, it is now required and assumed everywhere!
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 being written on demand basis, for custom needs at in-house projects. It has grown organically at the same time as the programmer has been studying the subject and learning the tools. So the code is quite messy in many places.
Features
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
Todo or In Progress
Improved documentation
Improved build tool
Autonomous tumor delineation/ROI placement
Regression classification
Requirements
Python 3.4
pathlib2 (or newer Python)
NumPy
SciPy
Optional requirements
leastsqbound-scipy (fitting)
Matplotlib (plotting)
Pydicom (reading DICOM files)
h5py (handling HDF5 files)
scikit-image (texture analysis)
Mahotas (texture analysis)
PyDoIt (task management)
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