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Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

  • Alexandre Routier
  • , Ninon Burgos
  • , Mauricio Díaz
  • , Michael Bacci
  • , Simona Bottani
  • , Omar El-Rifai
  • , Sabrina Fontanella
  • , Pietro Gori
  • , Jérémy Guillon
  • , Alexis Guyot
  • , Ravi Hassanaly
  • , Thomas Jacquemont
  • , Pascal Lu
  • , Arnaud Marcoux
  • , Tristan Moreau
  • , Jorge Samper-González
  • , Marc Teichmann
  • , Elina Thibeau-Sutre
  • , Ghislain Vaillant
  • , Junhao Wen
  • Adam Wild, Marie Odile Habert, Stanley Durrleman, Olivier Colliot
  • INRIA Institut National de Recherche en Informatique et en Automatique
  • Sorbonne Université
  • Institut du Cerveau et de la Moelle épinière (ICM)
  • INSERM U869
  • CNRS
  • AP-HP
  • Centre d'Acquisition et Traitement des Images (CATI Platform)

Résultats de recherche: Contribution à un journalArticleRevue par des pairs

109 Citations (Scopus)

Résumé

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

langue originaleAnglais
Numéro d'article689675
journalFrontiers in Neuroinformatics
Volume15
Les DOIs
étatPublié - 13 août 2021

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