Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge

被引：75

作者：

Isgum, Ivana ^{[1
]}

Benders, Manon J. N. L. ^{[2
]}

Avants, Brian ^{[3
]}

Cardoso, M. Jorge ^{[4
]}

Counsell, Serena J. ^{[5
]}

Gomez, Elda Fischi ^{[6
,7
]}

Gui, Laura ^{[6
]}

Huppi, Petra S. ^{[6
]}

Kersbergen, Karina J. ^{[2
]}

Makropoulos, Antonios ^{[5
,8
]}

Melbourne, Andrew ^{[4
]}

Moeskops, Pim ^{[1
]}

Mol, Christian P. ^{[1
]}

Kuklisova-Murgasova, Maria ^{[5
]}

Rueckert, Daniel ^{[8
]}

Schnabel, Julia A. ^{[9
]}

Srhoj-Egekher, Vedran ^{[10
]}

Wu, Jue ^{[3
]}

Wang, Siying ^{[9
]}

de Vries, Linda S. ^{[2
]}

Viergever, Max A. ^{[1
]}

机构：

[1] Univ Med Ctr Utrecht, Image Sci Inst, Utrecht, Netherlands

[2] Univ Med Ctr Utrecht, Dept Neonatol, Utrecht, Netherlands

[3] Univ Penn, Penn Image Comp & Sci Lab, Philadelphia, PA 19104 USA

[4] UCL, Ctr Med Image Comp, London WC1E 6BT, England

[5] Kings Coll London, Div Imaging Sci & Biomed Engn, Ctr Dev Brain, London WC2R 2LS, England

[6] Univ Geneva, Dept Pediat, Div Dev & Growth, CH-1211 Geneva 4, Switzerland

[7] Ecole Polytech Fed Lausanne, Signal Proc Lab LTS5, CH-1015 Lausanne, Switzerland

[8] Univ London Imperial Coll Sci Technol & Med, Biomed Image Anal Grp, London SW7 2AZ, England

[9] Univ Oxford, Dept Engn Sci, Inst Biomed Engn, Oxford OX1 2JD, England

[10] Univ Zagreb, Fac Elect Engn & Comp, Zagreb 41000, Croatia

来源：

MEDICAL IMAGE ANALYSIS | 2015年 / 20卷 / 01期

基金：

英国工程与自然科学研究理事会; 英国惠康基金;

关键词：

Neonatal brain; MRI; Brain segmentation; Segmentation evaluation; Segmentation comparison; COMPUTER-AIDED DETECTION; IMAGE REGISTRATION; PREMATURE-INFANTS; MR-IMAGES; TERM;

D O I：

10.1016/j.media.2014.11.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A number of algorithms for brain segmentation in preterm born infants have been published, but a reliable comparison of their performance is lacking. The NeoBrainS12 study (http://neobrains12.isi.uu.nl), providing three different image sets of preterm born infants, was set up to provide such a comparison. These sets are (i) axial scans acquired at 40 weeks corrected age, (ii) coronal scans acquired at 30 weeks corrected age and (iii) corona] scans acquired at 40 weeks corrected age. Each of these three sets consists of three T1- and T2-weighted MR images of the brain acquired with a 3T MRI scanner. The task was to segment cortical grey matter, non-myelinated and myelinated white matter, brainstem, basal ganglia and thalami, cerebellum, and cerebrospinal fluid in the ventricles and in the extracerebral space separately. Any team could upload the results and all segmentations were evaluated in the same way. This paper presents the results of eight participating teams. The results demonstrate that the participating methods were able to segment all tissue classes well, except myelinated white matter. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：135 / 151

页数：17

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