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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Article dans une revue
Int J Comput Assisted Radiol Surg, 2015, pp.1/16
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https://hal.archives-ouvertes.fr/hal-01181348
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Soumis le : jeudi 30 juillet 2015 - 08:35:22
Dernière modification le : jeudi 25 octobre 2018 - 01:10:23
Document(s) archivé(s) le : samedi 31 octobre 2015 - 10:25:07

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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. Int J Comput Assisted Radiol Surg, 2015, pp.1/16. 〈hal-01181348〉

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