Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis

V. Popescu, M. Battaglini, W. S. Hoogstrate, S. C J Verfaillie, I. C. Sluimer, R. A. van Schijndel, B. W. van Dijk, K. S. Cover, D. L. Knol, M. Jenkinson, F. Barkhof, N. de Stefano, H. Vrenken, F. Barkhof, X. Montalban, F. Fazekas, M. Filippi, J. Frederiksen, L. Kappos, D. MillerJ. Palace, C. Polman, M. Rocca, A. Rovira, T. Yousry

Research output: Contribution to journalArticle

81 Citations (Scopus)

Abstract

Background: Brain atrophy studies often use FSL-BET (Brain Extraction Tool) as the first step of image processing. Default BET does not always give satisfactory results on 3DT1 MR images, which negatively impacts atrophy measurements. Finding the right alternative BET settings can be a difficult and time-consuming task, which can introduce unwanted variability. Aim: To systematically analyze the performance of BET in images of MS patients by varying its parameters and options combinations, and quantitatively comparing its results to a manual gold standard. Methods: Images from 159 MS patients were selected from different MAGNIMS consortium centers, and 16 different 3DT1 acquisition protocols at 1.5. T or 3. T. Before running BET, one of three pre-processing pipelines was applied: (1) no pre-processing, (2) removal of neck slices, or (3) additional N3 inhomogeneity correction. Then BET was applied, systematically varying the fractional intensity threshold (the "f" parameter) and with either one of the main BET options ("B" - bias field correction and neck cleanup, "R" - robust brain center estimation, or "S" - eye and optic nerve cleanup) or none. For comparison, intracranial cavity masks were manually created for all image volumes. FSL-FAST (FMRIB's Automated Segmentation Tool) tissue-type segmentation was run on all BET output images and on the image volumes masked with the manual intracranial cavity masks (thus creating the gold-standard tissue masks). The resulting brain tissue masks were quantitatively compared to the gold standard using Dice overlap coefficient (DOC). Normalized brain volumes (NBV) were calculated with SIENAX. NBV values obtained using for SIENAX other BET settings than default were compared to gold standard NBV with the paired t-test. Results: The parameter/preprocessing/options combinations resulted in 20,988 BET runs. The median DOC for default BET (f = 0.5, g = 0) was 0.913 (range 0.321-0.977) across all 159 native scans. For all acquisition protocols, brain extraction was substantially improved for lower values of "f" than the default value. Using native images, optimum BET performance was observed for f = 0.2 with option "B", giving median DOC = 0.979 (range 0.867-0.994). Using neck removal before BET, optimum BET performance was observed for f = 0.1 with option "B", giving median DOC 0.983 (range 0.844-0.996). Using the above BET-options for SIENAX instead of default, the NBV values obtained from images after neck removal with f = 0.1 and option "B" did not differ statistically from NBV values obtained with gold-standard. Conclusion: Although default BET performs reasonably well on most 3DT1 images of MS patients, the performance can be improved substantially. The removal of the neck slices, either externally or within BET, has a marked positive effect on the brain extraction quality. BET option "B" with f = 0.1 after removal of the neck slices seems to work best for all acquisition protocols.

Original languageEnglish
Pages (from-to)1484-1494
Number of pages11
JournalNeuroImage
Volume61
Issue number4
DOIs
Publication statusPublished - Jul 16 2012

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Multiple Sclerosis
Brain
Neck
Masks
Gold
Atrophy

Keywords

  • BET
  • Brain extraction
  • FSL
  • MRI
  • Multiple sclerosis
  • Segmentation

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Popescu, V., Battaglini, M., Hoogstrate, W. S., Verfaillie, S. C. J., Sluimer, I. C., van Schijndel, R. A., ... Yousry, T. (2012). Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis. NeuroImage, 61(4), 1484-1494. https://doi.org/10.1016/j.neuroimage.2012.03.074

Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis. / Popescu, V.; Battaglini, M.; Hoogstrate, W. S.; Verfaillie, S. C J; Sluimer, I. C.; van Schijndel, R. A.; van Dijk, B. W.; Cover, K. S.; Knol, D. L.; Jenkinson, M.; Barkhof, F.; de Stefano, N.; Vrenken, H.; Barkhof, F.; Montalban, X.; Fazekas, F.; Filippi, M.; Frederiksen, J.; Kappos, L.; Miller, D.; Palace, J.; Polman, C.; Rocca, M.; Rovira, A.; Yousry, T.

In: NeuroImage, Vol. 61, No. 4, 16.07.2012, p. 1484-1494.

Research output: Contribution to journalArticle

Popescu, V, Battaglini, M, Hoogstrate, WS, Verfaillie, SCJ, Sluimer, IC, van Schijndel, RA, van Dijk, BW, Cover, KS, Knol, DL, Jenkinson, M, Barkhof, F, de Stefano, N, Vrenken, H, Barkhof, F, Montalban, X, Fazekas, F, Filippi, M, Frederiksen, J, Kappos, L, Miller, D, Palace, J, Polman, C, Rocca, M, Rovira, A & Yousry, T 2012, 'Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis', NeuroImage, vol. 61, no. 4, pp. 1484-1494. https://doi.org/10.1016/j.neuroimage.2012.03.074
Popescu V, Battaglini M, Hoogstrate WS, Verfaillie SCJ, Sluimer IC, van Schijndel RA et al. Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis. NeuroImage. 2012 Jul 16;61(4):1484-1494. https://doi.org/10.1016/j.neuroimage.2012.03.074
Popescu, V. ; Battaglini, M. ; Hoogstrate, W. S. ; Verfaillie, S. C J ; Sluimer, I. C. ; van Schijndel, R. A. ; van Dijk, B. W. ; Cover, K. S. ; Knol, D. L. ; Jenkinson, M. ; Barkhof, F. ; de Stefano, N. ; Vrenken, H. ; Barkhof, F. ; Montalban, X. ; Fazekas, F. ; Filippi, M. ; Frederiksen, J. ; Kappos, L. ; Miller, D. ; Palace, J. ; Polman, C. ; Rocca, M. ; Rovira, A. ; Yousry, T. / Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis. In: NeuroImage. 2012 ; Vol. 61, No. 4. pp. 1484-1494.
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title = "Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis",
abstract = "Background: Brain atrophy studies often use FSL-BET (Brain Extraction Tool) as the first step of image processing. Default BET does not always give satisfactory results on 3DT1 MR images, which negatively impacts atrophy measurements. Finding the right alternative BET settings can be a difficult and time-consuming task, which can introduce unwanted variability. Aim: To systematically analyze the performance of BET in images of MS patients by varying its parameters and options combinations, and quantitatively comparing its results to a manual gold standard. Methods: Images from 159 MS patients were selected from different MAGNIMS consortium centers, and 16 different 3DT1 acquisition protocols at 1.5. T or 3. T. Before running BET, one of three pre-processing pipelines was applied: (1) no pre-processing, (2) removal of neck slices, or (3) additional N3 inhomogeneity correction. Then BET was applied, systematically varying the fractional intensity threshold (the {"}f{"} parameter) and with either one of the main BET options ({"}B{"} - bias field correction and neck cleanup, {"}R{"} - robust brain center estimation, or {"}S{"} - eye and optic nerve cleanup) or none. For comparison, intracranial cavity masks were manually created for all image volumes. FSL-FAST (FMRIB's Automated Segmentation Tool) tissue-type segmentation was run on all BET output images and on the image volumes masked with the manual intracranial cavity masks (thus creating the gold-standard tissue masks). The resulting brain tissue masks were quantitatively compared to the gold standard using Dice overlap coefficient (DOC). Normalized brain volumes (NBV) were calculated with SIENAX. NBV values obtained using for SIENAX other BET settings than default were compared to gold standard NBV with the paired t-test. Results: The parameter/preprocessing/options combinations resulted in 20,988 BET runs. The median DOC for default BET (f = 0.5, g = 0) was 0.913 (range 0.321-0.977) across all 159 native scans. For all acquisition protocols, brain extraction was substantially improved for lower values of {"}f{"} than the default value. Using native images, optimum BET performance was observed for f = 0.2 with option {"}B{"}, giving median DOC = 0.979 (range 0.867-0.994). Using neck removal before BET, optimum BET performance was observed for f = 0.1 with option {"}B{"}, giving median DOC 0.983 (range 0.844-0.996). Using the above BET-options for SIENAX instead of default, the NBV values obtained from images after neck removal with f = 0.1 and option {"}B{"} did not differ statistically from NBV values obtained with gold-standard. Conclusion: Although default BET performs reasonably well on most 3DT1 images of MS patients, the performance can be improved substantially. The removal of the neck slices, either externally or within BET, has a marked positive effect on the brain extraction quality. BET option {"}B{"} with f = 0.1 after removal of the neck slices seems to work best for all acquisition protocols.",
keywords = "BET, Brain extraction, FSL, MRI, Multiple sclerosis, Segmentation",
author = "V. Popescu and M. Battaglini and Hoogstrate, {W. S.} and Verfaillie, {S. C J} and Sluimer, {I. C.} and {van Schijndel}, {R. A.} and {van Dijk}, {B. W.} and Cover, {K. S.} and Knol, {D. L.} and M. Jenkinson and F. Barkhof and {de Stefano}, N. and H. Vrenken and F. Barkhof and X. Montalban and F. Fazekas and M. Filippi and J. Frederiksen and L. Kappos and D. Miller and J. Palace and C. Polman and M. Rocca and A. Rovira and T. Yousry",
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pages = "1484--1494",
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TY - JOUR

T1 - Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis

AU - Popescu, V.

AU - Battaglini, M.

AU - Hoogstrate, W. S.

AU - Verfaillie, S. C J

AU - Sluimer, I. C.

AU - van Schijndel, R. A.

AU - van Dijk, B. W.

AU - Cover, K. S.

AU - Knol, D. L.

AU - Jenkinson, M.

AU - Barkhof, F.

AU - de Stefano, N.

AU - Vrenken, H.

AU - Barkhof, F.

AU - Montalban, X.

AU - Fazekas, F.

AU - Filippi, M.

AU - Frederiksen, J.

AU - Kappos, L.

AU - Miller, D.

AU - Palace, J.

AU - Polman, C.

AU - Rocca, M.

AU - Rovira, A.

AU - Yousry, T.

PY - 2012/7/16

Y1 - 2012/7/16

N2 - Background: Brain atrophy studies often use FSL-BET (Brain Extraction Tool) as the first step of image processing. Default BET does not always give satisfactory results on 3DT1 MR images, which negatively impacts atrophy measurements. Finding the right alternative BET settings can be a difficult and time-consuming task, which can introduce unwanted variability. Aim: To systematically analyze the performance of BET in images of MS patients by varying its parameters and options combinations, and quantitatively comparing its results to a manual gold standard. Methods: Images from 159 MS patients were selected from different MAGNIMS consortium centers, and 16 different 3DT1 acquisition protocols at 1.5. T or 3. T. Before running BET, one of three pre-processing pipelines was applied: (1) no pre-processing, (2) removal of neck slices, or (3) additional N3 inhomogeneity correction. Then BET was applied, systematically varying the fractional intensity threshold (the "f" parameter) and with either one of the main BET options ("B" - bias field correction and neck cleanup, "R" - robust brain center estimation, or "S" - eye and optic nerve cleanup) or none. For comparison, intracranial cavity masks were manually created for all image volumes. FSL-FAST (FMRIB's Automated Segmentation Tool) tissue-type segmentation was run on all BET output images and on the image volumes masked with the manual intracranial cavity masks (thus creating the gold-standard tissue masks). The resulting brain tissue masks were quantitatively compared to the gold standard using Dice overlap coefficient (DOC). Normalized brain volumes (NBV) were calculated with SIENAX. NBV values obtained using for SIENAX other BET settings than default were compared to gold standard NBV with the paired t-test. Results: The parameter/preprocessing/options combinations resulted in 20,988 BET runs. The median DOC for default BET (f = 0.5, g = 0) was 0.913 (range 0.321-0.977) across all 159 native scans. For all acquisition protocols, brain extraction was substantially improved for lower values of "f" than the default value. Using native images, optimum BET performance was observed for f = 0.2 with option "B", giving median DOC = 0.979 (range 0.867-0.994). Using neck removal before BET, optimum BET performance was observed for f = 0.1 with option "B", giving median DOC 0.983 (range 0.844-0.996). Using the above BET-options for SIENAX instead of default, the NBV values obtained from images after neck removal with f = 0.1 and option "B" did not differ statistically from NBV values obtained with gold-standard. Conclusion: Although default BET performs reasonably well on most 3DT1 images of MS patients, the performance can be improved substantially. The removal of the neck slices, either externally or within BET, has a marked positive effect on the brain extraction quality. BET option "B" with f = 0.1 after removal of the neck slices seems to work best for all acquisition protocols.

AB - Background: Brain atrophy studies often use FSL-BET (Brain Extraction Tool) as the first step of image processing. Default BET does not always give satisfactory results on 3DT1 MR images, which negatively impacts atrophy measurements. Finding the right alternative BET settings can be a difficult and time-consuming task, which can introduce unwanted variability. Aim: To systematically analyze the performance of BET in images of MS patients by varying its parameters and options combinations, and quantitatively comparing its results to a manual gold standard. Methods: Images from 159 MS patients were selected from different MAGNIMS consortium centers, and 16 different 3DT1 acquisition protocols at 1.5. T or 3. T. Before running BET, one of three pre-processing pipelines was applied: (1) no pre-processing, (2) removal of neck slices, or (3) additional N3 inhomogeneity correction. Then BET was applied, systematically varying the fractional intensity threshold (the "f" parameter) and with either one of the main BET options ("B" - bias field correction and neck cleanup, "R" - robust brain center estimation, or "S" - eye and optic nerve cleanup) or none. For comparison, intracranial cavity masks were manually created for all image volumes. FSL-FAST (FMRIB's Automated Segmentation Tool) tissue-type segmentation was run on all BET output images and on the image volumes masked with the manual intracranial cavity masks (thus creating the gold-standard tissue masks). The resulting brain tissue masks were quantitatively compared to the gold standard using Dice overlap coefficient (DOC). Normalized brain volumes (NBV) were calculated with SIENAX. NBV values obtained using for SIENAX other BET settings than default were compared to gold standard NBV with the paired t-test. Results: The parameter/preprocessing/options combinations resulted in 20,988 BET runs. The median DOC for default BET (f = 0.5, g = 0) was 0.913 (range 0.321-0.977) across all 159 native scans. For all acquisition protocols, brain extraction was substantially improved for lower values of "f" than the default value. Using native images, optimum BET performance was observed for f = 0.2 with option "B", giving median DOC = 0.979 (range 0.867-0.994). Using neck removal before BET, optimum BET performance was observed for f = 0.1 with option "B", giving median DOC 0.983 (range 0.844-0.996). Using the above BET-options for SIENAX instead of default, the NBV values obtained from images after neck removal with f = 0.1 and option "B" did not differ statistically from NBV values obtained with gold-standard. Conclusion: Although default BET performs reasonably well on most 3DT1 images of MS patients, the performance can be improved substantially. The removal of the neck slices, either externally or within BET, has a marked positive effect on the brain extraction quality. BET option "B" with f = 0.1 after removal of the neck slices seems to work best for all acquisition protocols.

KW - BET

KW - Brain extraction

KW - FSL

KW - MRI

KW - Multiple sclerosis

KW - Segmentation

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