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Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations
Author(s) -
Valverde Sergi,
Oliver Arnau,
Cabezas Mariano,
Roura Eloy,
Lladó Xavier
Publication year - 2015
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.24517
Subject(s) - voxel , segmentation , ground truth , computer science , pattern recognition (psychology) , artificial intelligence , pairwise comparison , data set , white matter , brain tissue , sørensen–dice coefficient , image segmentation , magnetic resonance imaging , biomedical engineering , medicine , radiology
Purpose Ground‐truth annotations from the well‐known Internet Brain Segmentation Repository (IBSR) datasets consider Sulcal cerebrospinal fluid (SCSF) voxels as gray matter. This can lead to bias when evaluating the performance of tissue segmentation methods. In this work we compare the accuracy of 10 brain tissue segmentation methods analyzing the effects of SCSF ground‐truth voxels on accuracy estimations. Materials and Methods The set of methods is composed by FAST, SPM5, SPM8, GAMIXTURE, ANN, FCM, KNN, SVPASEG, FANTASM, and PVC. Methods are evaluated using original IBSR ground‐truth and ranked by means of their performance on pairwise comparisons using permutation tests. Afterward, the evaluation is repeated using IBSR ground‐truth without considering SCSF. Results The Dice coefficient of all methods is affected by changes in SCSF annotations, especially on SPM5, SPM8 and FAST. When not considering SCSF voxels, SVPASEG (0.90 ± 0.01) and SPM8 (0.91 ± 0.01) are the methods from our study that appear more suitable for gray matter tissue segmentation, while FAST (0.89 ± 0.02) is the best tool for segmenting white matter tissue. Conclusion The performance and the accuracy of methods on IBSR images vary notably when not considering SCSF voxels. The fact that three of the most common methods (FAST, SPM5, and SPM8) report an important change in their accuracy suggest to consider these differences in labeling for new comparative studies. J. Magn. Reson. Imaging 2014 . © 2014 Wiley Periodicals, Inc. J. Magn. Reson. Imaging 2015;41:93–101. © 2014 Wiley Periodicals, Inc .