Abstract
Purpose
In both structural and functional MRI, there is a need for accurate and reliable automatic segmentation of brain regions. Inconsistent segmentation reduces sensitivity and may bias results in clinical studies. The current study compares the performance of publicly available segmentation tools and their impact on diffusion quantification, emphasizing the importance of using recently developed segmentation algorithms and imaging techniques.
Methods
Four publicly available, automatic segmentation methods (volBrain, FSL, FreeSurfer and SPM) are compared to manual segmentation of the thalamus and hippocampus imaged with a recently proposed T1-weighted MRI sequence (MP2RAGE). We evaluate morphometric accuracy on 22 healthy subjects and impact on diffusivity measurements obtained from aligned diffusion-weighted images on a subset of 10 subjects.
Results
Compared to manual segmentation, the highest Dice similarity index of the thalamus is obtained with volBrain using a local library (\(M=0.913\), \(\hbox {SD}=0.014\)) followed by volBrain using an external library (\(M=0.868\), \(\hbox {SD}=0.024\)), FSL (\({M}=0.806\), \(\mathrm{SD}=0.034\)), FreeSurfer (\({M}=0.798\), \(\mathrm{SD}=0.049\)) and SPM (\({M}=0.787\), \(\mathrm{SD}=0.031\)). The same order is found for hippocampus with volBrain local (\({M}=0.892\), \(\mathrm{SD}=0.016\)), volBrain external (\({M}=0.859\), \(\mathrm{SD}=0.014\)), FSL (\({M}=0.808\), \(\mathrm{SD}=0.017\)), FreeSurfer (\({M}=0.771\), \(\mathrm{SD}=0.023\)) and SPM (\({M}=0.735\), \(\mathrm{SD}=0.038\)). For diffusivity measurements, volBrain provides values closest to those obtained from manual segmentations. volBrain is the only method where FA values do not differ significantly from manual segmentation of the thalamus.
Conclusions
Overall we find that volBrain is superior in thalamus and hippocampus segmentation compared to FSL, FreeSurfer and SPM. Furthermore, the choice of segmentation technique and training library affects quantitative results from diffusivity measures in thalamus and hippocampus.
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Notes
See, for example, the recent MICCAI workshop on Multi-Atlas Labeling, https://masi.vuse.vanderbilt.edu/workshop2012/images/c/c8/MICCAI_2012_Workshop_v2.pdf.
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Acknowledgments
This work was funded in part by MINDLab UNIK initiative at Aarhus University, funded by the Danish Ministry of Science, Technology and Innovation, Grant Agreement Number 09-065250, partly by the Spanish grant TIN2013-43457-R from the Ministerio de Economia competitividad and with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Programme IdEx Bordeaux (ANR-10-IDEX-03-02) by funding HL-DTI grant, Cluster of excellence CPU, LaBEX TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi ImagIn”.
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All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was a retrospective study. For this type of study, formal consent is not required.
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Næss-Schmidt, E., Tietze, A., Blicher, J.U. et al. Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification. Int J CARS 11, 1979–1991 (2016). https://doi.org/10.1007/s11548-016-1433-0
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DOI: https://doi.org/10.1007/s11548-016-1433-0