Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations

Sergi Valverde*, Arnau Oliver, Mariano Cabezas, Eloy Roura, Xavier Lladó

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

79 Citations (Scopus)


Purpose: Ground-truth annotations from the wellknown 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 groundtruth 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 (0.9060.01) and SPM8 (0.9160.01) are the methods from our study that appear more suitable for gray matter tissue segmentation, while FAST (0.8960.02) 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.

Original languageEnglish
Pages (from-to)93-101
Number of pages9
JournalJournal of Magnetic Resonance Imaging
Issue number1
Publication statusPublished - Jan 2015
Externally publishedYes


  • brain MRI
  • IBSR
  • permutation tests
  • tissue segmentation


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