Benchmark on automatic 6-month-old infant brain segmentation algorithms: the iSeg-2017 challenge

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dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.contributor.author	Wang, Li
dc.contributor.author	Nie, Dong
dc.contributor.author	Li, Guannan
dc.contributor.author	Casamitjana Díaz, Adrià
dc.contributor.author	Vilaplana Besler, Verónica
dc.date	2019-02-27
dc.identifier.citation	Wang, L. [et al.]. Benchmark on automatic 6-month-old infant brain segmentation algorithms: the iSeg-2017 challenge. "IEEE transactions on medical imaging", 27 Febrer 2019, vol. 38, núm, 9, p. 2219-2230.
dc.identifier.citation	0278-0062
dc.identifier.citation	10.1109/TMI.2019.2901712
dc.identifier.uri	http://hdl.handle.net/2117/132560
dc.description.abstract	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstract	Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.
dc.description.abstract	Peer Reviewed
dc.language.iso	eng
dc.relation	https://ieeexplore.ieee.org/document/8654000
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject	Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Pediatria
dc.subject	Biomedical engineering
dc.subject	Nanotechnology
dc.subject	Infant
dc.subject	Brain
dc.subject	Segmentation
dc.subject	Isointense phase
dc.subject	Challenge
dc.subject	Enginyeria biomèdica
dc.subject	Nanotecnologia
dc.title	Benchmark on automatic 6-month-old infant brain segmentation algorithms: the iSeg-2017 challenge
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/article

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