| Autor: |
Gabriel Girard, Jonathan Rafael-Patiño, Raphaël Truffet, Dogu Baran Aydogan, Nagesh Adluru, Veena A. Nair, Vivek Prabhakaran, Barbara B. Bendlin, Andrew L. Alexander, Sara Bosticardo, Ilaria Gabusi, Mario Ocampo-Pineda, Matteo Battocchio, Zuzana Piskorova, Pietro Bontempi, Simona Schiavi, Alessandro Daducci, Aleksandra Stafiej, Dominika Ciupek, Fabian Bogusz, Tomasz Pieciak, Matteo Frigo, Sara Sedlar, Samuel Deslauriers-Gauthier, Ivana Kojčić, Mauro Zucchelli, Hiba Laghrissi, Yang Ji, Rachid Deriche, Kurt G Schilling, Bennett A. Landman, Alberto Cacciola, Gianpaolo Antonio Basile, Salvatore Bertino, Nancy Newlin, Praitayini Kanakaraj, Francois Rheault, Patryk Filipiak, Timothy M. Shepherd, Ying-Chia Lin, Dimitris G. Placantonakis, Fernando E. Boada, Steven H. Baete, Erick Hernández-Gutiérrez, Alonso Ramírez-Manzanares, Ricardo Coronado-Leija, Pablo Stack-Sánchez, Luis Concha, Maxime Descoteaux, Sina Mansour L．, Caio Seguin, Andrew Zalesky, Kenji Marshall, Erick J. Canales-Rodríguez, Ye Wu, Sahar Ahmad, Pew-Thian Yap, Antoine Théberge, Florence Gagnon, Frédéric Massi, Elda Fischi-Gomez, Rémy Gardier, Juan Luis Villarreal Haro, Marco Pizzolato, Emmanuel Caruyer, Jean-Philippe Thiran |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
NeuroImage, Vol 277, Iss , Pp 120231- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1095-9572 |
| DOI: |
10.1016/j.neuroimage.2023.120231 |
| Popis: |
Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn’t capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full Text from ScienceDirect