Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge.
作者： Ivana Išgum , Manon J.N.L. Benders , Brian Avants , M. Jorge Cardoso , Serena J. Counsell
DOI: 10.1016/J.MEDIA.2014.11.001
关键词:
摘要: A number of algorithms for brain segmentation in preterm born infants have been published, but a reliable comparison their performance is lacking. The NeoBrainS12 study (http://neobrains12.isi.uu.nl), providing three different image sets infants, was set up to provide such comparison. These are (i) axial scans acquired at 40 weeks corrected age, (ii) coronal 30 age and (iii) age. Each these consists T1- T2-weighted MR images the with 3T MRI scanner. task segment cortical grey matter, non-myelinated myelinated white brainstem, basal ganglia thalami, cerebellum, cerebrospinal fluid ventricles extracerebral space separately. Any team could upload results all segmentations were evaluated same way. This paper presents eight participating teams. demonstrate that methods able tissue classes well, except matter.
core.ac.uk
medicalimageanalysisjournal.com
elsevier.com参考文章(29)
B AVANTS, C EPSTEIN, M GROSSMAN, J GEE, Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis. ,vol. 12, pp. 26- 41 ,(2008) , 10.1016/J.MEDIA.2007.06.004
Laura Gui, Radoslaw Lisowski, Tamara Faundez, Petra S. Hüppi, François Lazeyras, Michel Kocher, Morphology-driven automatic segmentation of MR images of the neonatal brain. Medical Image Analysis. ,vol. 16, pp. 1565- 1579 ,(2012) , 10.1016/J.MEDIA.2012.07.006
Hui Xue, Latha Srinivasan, Shuzhou Jiang, Mary Rutherford, A. David Edwards, Daniel Rueckert, Joseph V. Hajnal, Automatic segmentation and reconstruction of the cortex from neonatal MRI. NeuroImage. ,vol. 38, pp. 461- 477 ,(2007) , 10.1016/J.NEUROIMAGE.2007.07.030
D. K. Thompson, S. K. Warfield, J. B. Carlin, M. Pavlovic, H. X. Wang, M. Bear, M. J. Kean, L. W. Doyle, G. F. Egan, T. E. Inder, Perinatal Risk Factors Altering Regional Brain Structure in the Preterm Infant. Brain. ,vol. 130, pp. 667- 677 ,(2007) , 10.1093/BRAIN/AWL277
Petronella Anbeek, Koen L Vincken, Floris Groenendaal, Annemieke Koeman, Matthias J P van Osch, Jeroen Van der Grond, Probabilistic Brain Tissue Segmentation in Neonatal Magnetic Resonance Imaging Pediatric Research. ,vol. 63, pp. 158- 163 ,(2008) , 10.1203/PDR.0B013E31815ED071
B. S. Peterson, A. W. Anderson, R. Ehrenkranz, L. H. Staib, M. Tageldin, E. Colson, J. C. Gore, C. C. Duncan, R. Makuch, L. R. Ment, Regional brain volumes and their later neurodevelopmental correlates in term and preterm infants. Pediatrics. ,vol. 111, pp. 939- 948 ,(2003) , 10.1542/PEDS.111.5.939
Tony F. Chan, B.Yezrielev Sandberg, Luminita A. Vese, Active Contours without Edges for Vector-Valued Images Journal of Visual Communication and Image Representation. ,vol. 11, pp. 130- 141 ,(2000) , 10.1006/JVCI.1999.0442
F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, P. Suetens, Multimodality image registration by maximization of mutual information IEEE Transactions on Medical Imaging. ,vol. 16, pp. 187- 198 ,(1997) , 10.1109/42.563664
M. Jorge Cardoso, Andrew Melbourne, Giles S. Kendall, Marc Modat, Nicola J. Robertson, Neil Marlow, Sebastien Ourselin, AdaPT: An adaptive preterm segmentation algorithm for neonatal brain MRI NeuroImage. ,vol. 65, pp. 97- 108 ,(2013) , 10.1016/J.NEUROIMAGE.2012.08.009
Maria Kuklisova-Murgasova, Paul Aljabar, Latha Srinivasan, Serena J. Counsell, Valentina Doria, Ahmed Serag, Ioannis S. Gousias, James P. Boardman, Mary A. Rutherford, A. David Edwards, Joseph V. Hajnal, Daniel Rueckert, A dynamic 4D probabilistic atlas of the developing brain NeuroImage. ,vol. 54, pp. 2750- 2763 ,(2011) , 10.1016/J.NEUROIMAGE.2010.10.019