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

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• 作者：Jose Dolz ; Anne Laprie ; Soléakhéna Ken…
• 关键词：Support vector machines ; Machine learning ; Supervised learning ; MRI segmentation ; Radiotherapy ; Brain cancer
• 刊名：International Journal of Computer Assisted Radiology and Surgery
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：11
• 期：1
• 页码：43-51
• 全文大小：1,360 KB
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• 作者单位：Jose Dolz (1) (2)
  Anne Laprie (3)
  Soléakhéna Ken (3)
  Henri-Arthur Leroy (2) (4)
  Nicolas Reyns (2) (4)
  Laurent Massoptier (1)
  Maximilien Vermandel (2)
  
  1. AQUILAB, Biocentre A. Fleming, 250 rue Salvador Allende, 59120, Loos les Lille, France
  2. Univ. Lille, Inserm, CHU Lille, U1189, ONCO-THAI - Image Assisted Laser Therapy for Oncology, 59000, Lille, France
  3. Department of Radiation Oncology, Institut Claudius Regaud, Toulouse, France
  4. Neurosurgery Department, University Hospital Lille, Lille, France
  
• 刊物主题：Imaging / Radiology; Surgery; Health Informatics; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Science, general;
• 出版者：Springer Berlin Heidelberg
• ISSN：1861-6429

文摘

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).