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뇌과학이미징연구단
뇌과학 이미징 연구단
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Standardized assessment of automatic segmentation of white matter hyperintensities; Results of the WMH segmentation challenge

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Title
Standardized assessment of automatic segmentation of white matter hyperintensities; Results of the WMH segmentation challenge
Author(s)
Hugo J. Kuijf; J. Matthijs Biesbroek; Jeroen De Bresser; Rutger Heinen; Simon Andermatt; Mariana Bento; Matt Berseth; Mikhail Belyaev; M. Jorge Cardoso; Adrià Casamitjana; D. Louis Collins; Mahsa Dadar; Achilleas Georgiou; Mohsen Ghafoorian; Dakai Jin; April Khademi; Jesse Knight; Hongwei Li; Xavier Lladó; Miguel Luna; Qaiser Mahmood; Richard McKinley; Alireza Mehrtash; Sébastien Ourselin; Bo-Yong Park; Hyunjin Park; Sang Hyun Park; Simon Pezold; Elodie Puybareau; Leticia Rittner; Carole H. Sudre; Sergi Valverde; Veronica Vilaplana; Roland Wiest; Yongchao Xu; Ziyue Xu; Guodong Zeng; Jianguo Zhang; Guoyan Zheng; Christopher Chen; Wiesje van der Flier; Frederik Barkhof; Max A. Viergever; Geert Jan Biessels
Publication Date
2019-11
Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING, v.38, no.11, pp.2556 - 2568
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
Abstract: Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation. © 2019 IEEE.
URI
https://pr.ibs.re.kr/handle/8788114/6790
ISSN
0278-0062
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > Journal Papers (저널논문)
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