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CemrgApp Version 2.0
- Motion Quantification
- Anatomical Measurements
- Scar Quantification
- Morphological Measurements
- Electrophysiology Simulations
- Linux
- macOS
- Microsoft Windows
- CMake 3.16 or newer
- Qt 5.11.1
- MITK 2018.04.2
To run all of CemrgApp v2.0 specialised modules, such as the machine learning pipeline for scar quantification, Docker is required to be installed on the user's computer. We also use Docker to expand and collaborate with other centres' software. A more detailed explanation for downloading and configuring Docker can be found in this wiki, under the prerequisites section.
Please note that a bug in MITK v2018.04.2 prevents the DICOM reader from working correctly. This should be addressed in an upcoming release of MITK, but unfortunately at this moment CemrgApp executable binaries for macOS struggle with some DICOM datasets. If you find yourself with this problem, CemrgApp still functions normally and can read NIfTI (.nii) files with ease. Our suggestion would be for you to convert your DICOMs into NIFTIs externally and then use them directly in CemrgApp. A good python based alternative for this is dicom2nitfi, available through CemrgApp, pip, and conda installations.
If the alternative dicom2nifti is not available, then you can try the DICOM dictionary workaround:
- Download the file: dicom.dic
- Open a Terminal
- Type
export DCMDICTPATH=/path/to/dicom.dic - Start CemrgApp from Terminal:
/Applications/CemrgApp/CemrgApp.app/Contents/MacOS/CemrgApp
You can also check our more detailed step by step guide.
The Cardiac Electro-Mechanics Research Group (CEMRG) at King's College London applies statistical, machine learning and simulation approaches to combine experimental and clinical data with physics and biology to study the physiology, pathology, diagnosis and treatment of the heart.