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Supervised machine learning based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context

Abstract : Purpose To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk (OARs) 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).
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https://hal.archives-ouvertes.fr/hal-01181348
Contributor : Serge Mordon <>
Submitted on : Thursday, July 30, 2015 - 8:35:22 AM
Last modification on : Friday, March 12, 2021 - 11:10:04 AM
Long-term archiving on: : Saturday, October 31, 2015 - 10:25:07 AM

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  • HAL Id : hal-01181348, version 1

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J. Dolz, Anne Laprie, Soléakhéna Ken, Henri-Arthur Leroy, Nicolas Reyns, et al.. Supervised machine learning based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context. International Journal of Computer Assisted Radiology and Surgery, Springer Verlag, 2015, pp.1/16. ⟨hal-01181348⟩

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