Abstract
Purpose
To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI).
Methods
SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours.
Results
Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below \(1.5\,\hbox {cm}^{3}\), where the value for best performing IIVs configuration was \(0.85\,\hbox {cm}^{3}\), representing an absolute mean difference of \(3.99\,\%\) with respect to the manual segmented volumes.
Conclusion
Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.
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Acknowledgments
This project has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under Grant Agreement No. PITN-GA-2011-290148.
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Jose Dolz, Solakhna Ken, Henri-Arthur Leroy, Nicolas Reyns, Anne Laprie, Laurent Massoptier and Maximilen Vermandel declare that they have no conflict of interest.
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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).
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Dolz, J., Laprie, A., Ken, S. et al. Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context. Int J CARS 11, 43–51 (2016). https://doi.org/10.1007/s11548-015-1266-2
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DOI: https://doi.org/10.1007/s11548-015-1266-2