Clinica is an open source software platform for clinical neuroscience research studies developed by the Aramis Lab. It provides a standardised file organisation; complex processing pipelines involving the combination of several image analysis software packages; feature extraction and analysis approaches based on statistics and machine learning.
More precisely, Clinica integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET; in the future, EEG/MEG. For each modality, Clinica allows the easy extraction of various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modelling, morphometry or network analysis methods. Processing pipelines are based on combinations of freely available tools developed by the community. It provides an integrated data management specification to store raw and processing data. Clinica is written in Python and uses the Nipype system for pipelining. It combines widely-used software packages for neuroimaging data analysis (SPM, Freesurfer, FSL, MRtrix, etc.), machine learning (Scikit-learn) and the BIDS standard for data organization. Overall, Clinica should help to: i) easily share data and results; ii) make research more reproducible; iii) spend less time on data management and processing.
I contribute to the management of the project and of the developers, to the writing of the documentation, to the development of the PET data analysis pipelines and to the testing.
NiftyWeb is a web service tool developed by the Translational Imaging Group (UCL) that provides an entry point to cutting edge medical image analysis algorithms. NiftyWeb has a friendly interface and allows anyone to test or use the algorithms with minimal effort and with their optimal configurations. The website currently runs on a distributed network, where different nodes are responsible for specific algorithms. Under the current implementation, seven algorithms are available:
- STEPS, which computes a brain skull stripping mask or hippocampal masks depending on the user selection;
- Boundary Shift Integral (BSI), which computes the atrophy between two time-points and generates a PDF report as a result;
- Spinal cord cross-sectional area, which computes the cross-sectional area for each slice of a binarised segmentation of the spinal cord;
- Filling lesions, which can take any image modality and a mask to inpaint lesions;
- GIF, which computes the brain parcellation and tissue segmentation from a T1 image;
- pCT, which computes a pseudo CT image from a T1or T2 image;
- Whole heart segmentation, which segments the whole heart and the great vessels;
- MRI to 3D printing conversion, which extracts the mesh of a whole brain, any specific brain tissue or any particular area from a magnetic resonance image.
I contributed to the creation of the pCT web service tool, which enables the fully automated synthesis of CT images from MRI data. This tool has already been used more than 2000 times by more than 10 different clinical and research groups around the world.
NiftySeg is an open source image segmentation and parcellation software developed by the Translational Imaging Group (UCL) with 6000+ downloads from 43 countries. I contributed to the creation of novel algorithms for image synthesis based on a multi-atlas propagation and fusion technique. My contribution mainly focused on the fusion part of the approach, where the propagated atlases are combined locally by computing for each voxel a local similarity measure between each deformed atlas and the target subject.