Structural connectivity estimates are accurate: the outcome of diffusion-simulated connectivity (DiSCo) challenge
Gabriel Girard1,2,3, Jonathan Rafael-Patino2,3, Raphaël Truffet4, Dogu Baran Aydogan5,6,7, Nagesh Adluru8,9, Veena A. Nair9, Vivek Prabhakaran9, Barbara B. Bendlin10, Andrew L. Alexander8,11,12, Sara Bosticardo13,14, Ilaria Gabusi13,15, Mario Ocampo-Pineda13, Matteo Battocchio13,16, Zuzana Piskorova13,17, Pietro Bontempil13,18, Simona Schiavi19, Alessandro Daducci13, Aleksandra Stafiej20, Dominika Ciupek20, Fabian Bogusz20, Tomasz Pieciak21, Matteo Frigo22, Sara Sedlar22, Samuel Deslauriers-Gauthier22, Ivana Kojcic22, Mauro Zucchelli22, Hiba Laghrissi22, Yang Ji22, Rachid Deriche22, Kurt G Schilling23, Bennett A Landman23,24, Alberto Cacciola25, Gianpaolo Antonio Basile25, Salvatore Bertino25, Nancy Newlin24, Praitayini Kanakaraj24, Francois Rheault24, Patryk Filipiak26, Timothy Shepherd26, Ying-Chia Lin26, Dimitris G Placantonakis27, Fernando E Boada26, Steven H Baete26, Erick Hernández-Gutiérrez16, Alonso Ramírez-Manzanares28, Ricardo Coronado-Leija29, Pablo Stack-Sánchez28, Luis Concha30, Maxime Descoteaux16, Sina Mansour L31,32, Caio Seguin32,33, Andrew Zalesky31,32, Kenji Marshall3,34, Erick J Canales-Rodríguez3, Ye Wu35, Sahar Ahmad35, Pew-Thian Yap35, Antoine Théberge16, Florence Gagnon16, Frédéric Massi16, Juan Luis Villarreal Haro3, Marco Pizzolato3,36, Emmanuel Caruyer4, and Jean-Philippe Thiran1,2,3
1CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 2Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland, 3Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, France, 5A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland, 6Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland, 7Department of Psychiatry, Helsinki University Hospital, Helsinki, Finland, 8Waisman Center, University of Wisconsin-Madison, Madison, WI, United States, 9Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 10Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States, 11Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 12Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States, 13Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy, 14Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, 15Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy, 16Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada, 17Brno Faculty of Electrical Engineering and Communication, Department of mathematics, University of Technology, Brno, Czech Republic, 18Department of neurosciences, biomedicine and movement sciences, University of Verona, Verona, Italy, 19Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy, 20AGH University of Science and Technology, Kraków, Poland, 21Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, Valladolid, Spain, 22Athena Project Team, Centre Inria d'Université Côte d'Azur, Sophia Antipolis, France, 23Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, 24Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States, 25Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy, 26Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States, 27Department of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU Langone Health, New York, NY, United States, 28Computer Science Department. Centro de Investigación en Matemáticas A.C., Guanajuato, Mexico, 29Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, United States, 30Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Mexico, 31Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia, 32Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Australia, 33The University of Sydney, School of Biomedical Engineering, Sydney, Australia, 34McGill University, Montréal, QC, Canada, 35Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 36Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
The estimation of structural connectivity from DW-MRI is a challenging task, in part due to false-positive connections. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was organized with the aim of evaluating connectivity methods using large-scale synthetic datasets obtained from DW-MRI Monte-Carlo simulations. The outcome of the challenge suggests that methods selected by the 14 teams participating in the challenge provide both high correlations between estimated and ground-truth connectivity weights and high accuracy in binary connectivity identification. Furthermore, the challenge provided unique data with realistic connectivity and microstructure properties to foster the development of connectivity estimation methods.
This abstract and the presentation materials are available to members only;
a login is required.