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IRIS
: White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?
Schilling, Kurt G.;Rheault, François;Petit, Laurent;Hansen, Colin B.;Nath, Vishwesh;Yeh, Fang-Cheng;Girard, Gabriel;Barakovic, Muhamed;Rafael-Patino, Jonathan;Yu, Thomas;Fischi-Gomez, Elda;Pizzolato, Marco;Ocampo-Pineda, Mario;Schiavi, Simona;Canales-Rodríguez, Erick J.;Daducci, Alessandro;Granziera, Cristina;Innocenti, Giorgio;Thiran, Jean-Philippe;Mancini, Laura;Wastling, Stephen;Cocozza, Sirio;Petracca, Maria;Pontillo, Giuseppe;Mancini, Matteo;Vos, Sjoerd B.;Vakharia, Vejay N.;Duncan, John S.;Melero, Helena;Manzanedo, Lidia;Sanz-Morales, Emilio;Peña-Melián, Ángel;Calamante, Fernando;Attyé, Arnaud;Cabeen, Ryan P.;Korobova, Laura;Toga, Arthur W.;Vijayakumari, Anupa Ambili;Parker, Drew;Verma, Ragini;Radwan, Ahmed;Sunaert, Stefan;Emsell, Louise;De Luca, Alberto;Leemans, Alexander;Bajada, Claude J.;Haroon, Hamied;Azadbakht, Hojjatollah;Chamberland, Maxime;Genc, Sila;Tax, Chantal M. W.;Yeh, Ping-Hong;Srikanchana, Rujirutana;Mcknight, Colin D.;Yang, Joseph Yuan-Mou;Chen, Jian;Kelly, Claire E.;Yeh, Chun-Hung;Cochereau, Jerome;Maller, Jerome J.;Welton, Thomas;Almairac, Fabien;Seunarine, Kiran K;Clark, Chris A.;Zhang, Fan;Makris, Nikos;Golby, Alexandra;Rathi, Yogesh;O'Donnell, Lauren J.;Xia, Yihao;Aydogan, Dogu Baran;Shi, Yonggang;Fernandes, Francisco Guerreiro;Raemaekers, Mathijs;Warrington, Shaun;Michielse, Stijn;Ramírez-Manzanares, Alonso;Concha, Luis;Aranda, Ramón;Meraz, Mariano Rivera;Lerma-Usabiaga, Garikoitz;Roitman, Lucas;Fekonja, Lucius S.;Calarco, Navona;Joseph, Michael;Nakua, Hajer;Voineskos, Aristotle N.;Karan, Philippe;Grenier, Gabrielle;Legarreta, Jon Haitz;Adluru, Nagesh;Nair, Veena A.;Prabhakaran, Vivek;Alexander, Andrew L.;Kamagata, Koji;Saito, Yuya;Uchida, Wataru;Andica, Christina;Abe, Masahiro;Bayrak, Roza G.;Wheeler-Kingshott, Claudia A. M. Gandini;D'Angelo, Egidio;Palesi, Fulvia;Savini, Giovanni;Rolandi, Nicolò;Guevara, Pamela;Houenou, Josselin;López-López, Narciso;Mangin, Jean-François;Poupon, Cyril;Román, Claudio;Vázquez, Andrea;Maffei, Chiara;Arantes, Mavilde;Andrade, José Paulo;Silva, Susana Maria;Calhoun, Vince D.;Caverzasi, Eduardo;Sacco, Simone;Lauricella, Michael;Pestilli, Franco;Bullock, Daniel;Zhan, Yang;Brignoni-Perez, Edith;Lebel, Catherine;Reynolds, Jess E;Nestrasil, Igor;Labounek, René;Lenglet, Christophe;Paulson, Amy;Aulicka, Stefania;Heilbronner, Sarah R.;Heuer, Katja;Chandio, Bramsh Qamar;Guaje, Javier;Tang, Wei;Garyfallidis, Eleftherios;Raja, Rajikha;Anderson, Adam W.;Landman, Bennett A.;Descoteaux, Maxime
2021-01-01
Abstract
: White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11699/89291
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Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
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