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Article Dans Une Revue NeuroImage Année : 2023

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Gabriel Girard (1, 2, 3) , Jonathan Rafael-Patiño (1, 2, 3) , Raphaël Truffet (4) , Dogu Baran Aydogan (5, 6, 7) , Nagesh Adluru (8, 9) , Veena A Nair (9) , Vivek Prabhakaran (9) , Barbara B Bendlin (10) , Andrew L Alexander (8, 11, 12) , Sara Bosticardo (13) , Ilaria Gabusi (13) , Mario Ocampo-Pineda (13) , Matteo Battocchio (13) , Zuzana Piskorova (13) , Pietro Bontempi (13) , Simona Schiavi (13, 14) , Alessandro Daducci (13) , Aleksandra Stafiej (15) , Dominika Ciupek (16) , Fabian Bogusz (15) , Tomasz Pieciak (17) , Matteo Frigo (18) , Sara Sedlar (18) , Samuel Deslauriers-Gauthier (19, 20) , Ivana Kojčić (18) , Mauro Zucchelli (18) , Hiba Laghrissi (18) , Yang Ji (18) , Rachid Deriche (19) , Kurt G Schilling (21) , Bennett A Landman (21, 22) , Alberto Cacciola (23, 24) , Gianpaolo Antonio Basile (23) , Salvatore Bertino (23) , Nancy Newlin (21) , Praitayini Kanakaraj (21) , Francois Rheault (21) , Patryk Filipiak (25) , Timothy M Shepherd (25) , Ying-Chia Lin (25) , Dimitris G Placantonakis (25) , Fernando E Boada (26) , Steven H Baete (25) , Erick Hernández-Gutiérrez (27) , Alonso Ramírez-Manzanares (28) , Ricardo Coronado-Leija (25) , Pablo Stack-Sánchez (28) , Luis Concha (29) , Maxime Descoteaux (27) , Caio Seguin (30, 31) , Andrew Zalesky (30) , Kenji Marshall (1, 32) , Erick J Canales-Rodríguez (1) , Ye Wu (33) , Sahar Ahmad (33) , Pew-Thian Yap (33) , Antoine Théberge (27) , Florence Gagnon (27) , Frédéric Massi (27) , Elda Fischi-Gomez (3, 2, 1) , Rémy Gardier (1) , Juan Luis Villarreal Haro (1) , Marco Pizzolato (1) , Emmanuel Caruyer (4) , Jean-Philippe Thiran (3, 2, 1)
1 LTS5 - Signal Processing Laboratory [Lausanne]
2 CHUV - Centre Hospitalier Universitaire Vaudois = Lausanne University Hospital [Lausanne]
3 CIBM - Center for Biomedical Imaging [Lausanne]
4 EMPENN - Neuroimagerie: méthodes et applications
5 A.I. Virtanen Institute for Molecular Sciences
6 Department of Neuroscience and Biomedical Engineering [Espoo]
7 Department of Psychiatry [Helsinki]
8 Waisman Center [Madison]
9 Department of Radiology [Madison]
10 Department of Medicine [Madison]
11 Department of Medical Physics [Madison]
12 Department of Psychology and Psychiatry [Madison]
13 UNIVR | DI - Department of Computer Science [Verona]
14 UniGe - Università degli studi di Genova = University of Genoa
15 AGH-UST - Department of Automatics
16 Sano Centre for Computational Personalised Medicine [Kraków]
17 AGH UST - AGH University of Science and Technology [Krakow, PL]
18 ATHENA - Computational Imaging of the Central Nervous System
19 CRONOS - Modélisation des résaux dynamiques cérébraux
20 UniCA - Université Côte d'Azur
21 Vanderbilt University Medical Center [Nashville]
22 Department of Electrical Engineering & Computer Science [Nashville]
23 UniMe - Università degli Studi di Messina = University of Messina
24 THU - Tsinghua University [Beijing]
25 NYU Langone Health [New York]
26 Department of Radiology [Stanford]
27 UdeS - Département d'informatique [Sherbrooke]
28 CIMAT - Centro de Investigación en Matemáticas
29 UNAM - Universidad Nacional Autónoma de México = National Autonomous University of Mexico
30 University of Melbourne
31 The University of Sydney
32 McGill University = Université McGill [Montréal, Canada]
33 BRIC - Biomedical Research Imaging Center [North Carolina]

Résumé

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.
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hal-04390592 , version 1 (12-01-2024)

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Gabriel Girard, Jonathan Rafael-Patiño, Raphaël Truffet, Dogu Baran Aydogan, Nagesh Adluru, et al.. Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge. NeuroImage, 2023, 277, pp.1-11. ⟨10.1016/j.neuroimage.2023.120231⟩. ⟨hal-04390592⟩
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