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  1. AU="Michel M. Santos"
  2. AU=Chong Yong-Yeow
  3. AU="Posca-Maina, Marcela"
  4. AU="Trupin, Laura"
  5. AU="Bezerra, Antônio Diego M."
  6. AU="Xiao-hua Lin"
  7. AU="Ksenia Zaytseva" AU="Ksenia Zaytseva"
  8. AU="Michael N Alexis"
  9. AU="Rivero-Moreno, Yeisson"
  10. AU="Köllmann, Nienke"
  11. AU="Maveddat, Ashley"
  12. AU=Boerrigter Guido
  13. AU=Wajant H
  14. AU=Marsboom Glenn
  15. AU="Xuwei, Tao"
  16. AU="Matias, Ricardo"
  17. AU="Daly, Brian P."
  18. AU="Bissaro, Maicol"
  19. AU="Mateo, Mathieu"
  20. AU="Yousra Aouinati"
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  22. AU=CASTORENA-GONZALEZ JORGE A.
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  24. AU="José M. Ramada"
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Artikel ; Online: Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

Olivier Commowick / Audrey Istace / Michaël Kain / Baptiste Laurent / Florent Leray / Mathieu Simon / Sorina Camarasu Pop / Pascal Girard / Roxana Améli / Jean-Christophe Ferré / Anne Kerbrat / Thomas Tourdias / Frédéric Cervenansky / Tristan Glatard / Jérémy Beaumont / Senan Doyle / Florence Forbes / Jesse Knight / April Khademi /
Amirreza Mahbod / Chunliang Wang / Richard McKinley / Franca Wagner / John Muschelli / Elizabeth Sweeney / Eloy Roura / Xavier Lladó / Michel M. Santos / Wellington P. Santos / Abel G. Silva-Filho / Xavier Tomas-Fernandez / Hélène Urien / Isabelle Bloch / Sergi Valverde / Mariano Cabezas / Francisco Javier Vera-Olmos / Norberto Malpica / Charles Guttmann / Sandra Vukusic / Gilles Edan / Michel Dojat / Martin Styner / Simon K. Warfield / François Cotton / Christian Barillot

Scientific Reports, Vol 8, Iss 1, Pp 1-

2018  Band 17

Abstract: Abstract We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent ... ...

Abstract Abstract We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
Schlagwörter Medicine ; R ; Science ; Q
Thema/Rubrik (Code) 006
Sprache Englisch
Erscheinungsdatum 2018-09-01T00:00:00Z
Verlag Nature Publishing Group
Dokumenttyp Artikel ; Online
Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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