Adrià Casamitjana

Adrià Casamitjana

Resources

Web application

NextBrain interactive tool.
Authors: James Hughes, Adrià Casamitjana, Peter Schmidt, Juan Eugenio Iglesias

We present a next-generation probabilistic atlas of the human brain using histological sections of five full human hemispheres with manual annotations for 333 regions of interest. This website enables the interactive inspection of these five cases using a 3D navigation interface and search functionality.

Software

NextBrain segmentation tool
Main authors: Oula, Puonti, Juan Eugenio Iglesias; Collaborators: Adrià Casamitjana

I collaborated in the implementation of the NextBrain segmentation toolWe implemented a new neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging processing tools that perform statistical inference based only on linear models. NeAT is a modular, exible and user-friendly tool via command line interface that provides a range of statistical and machine learning non-linear methods for model estimation. Several metrics based on curve fitting and complexity are used for model inference. The toolbox includes a graphical user interface (GUI) for visualization of results.

USLR: unbiased and smooth longitudinal registration tool)
Main authors: Adrià Casamitjana

Here we implemented a longitudinal registration toolbox that consists of three sequential steps: (1) SynthSeg segmentation followed by bias field correction; (2) subject-wise linear registration; (3) subject-wise non-linear registration. The output of this pipeline contains a subject specific template and nonlinear trajectories across time. These outputs are then used for downstream applications such as longitudinal segmentation.

NeAT: A nonlinear neuroimaging analysis toolbox.

Authors: Adrià Casamitjana, Santi Puch, Asier Aduriz, Verónica Vilaplana, Juando Gispert, José Luis Molinuevo
We implemented a new neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging processing tools that perform statistical inference based only on linear models. NeAT is a modular, exible and user-friendly tool via command line interface that provides a range of statistical and machine learning non-linear methods for model estimation. Several metrics based on curve fitting and complexity are used for model inference. The toolbox includes a graphical user interface (GUI) for visualization of results. Other links: