Abstract: Fast MRI Whole Brain Segmentation with Fully Convolutional Neural Networks

  • Abhijit Guha Roy
  • Sailesh Conjeti
  • Nassir Navab
  • Christian Wachinger
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Whole brain segmentation from structural MRI-T1 scan is a prerequisite for most morphological analyses, but requires hours of processing time and therefore delays the availability of image markers after scan acquisition. We introduced a fully convolution neural network (F-CNN) that segments a brain scan in several seconds [1]. Training deep F-CNNs for semantic image segmentation requires access to abundant labeled data.

Literatur

  1. 1.
    Error corrective boosting for learning fully convolutional networks with limited data. Springer 2017.Google Scholar
  2. 2.
    Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. In Neuron; 2002.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Abhijit Guha Roy
    • 1
    • 2
  • Sailesh Conjeti
    • 2
    • 3
  • Nassir Navab
    • 2
    • 4
  • Christian Wachinger
    • 1
  1. 1.Artificial Intelligence in Medical Imaging (AI-Med)KJP, LMU MünchenMünchenDeutschland
  2. 2.Computer Aided Medical ProceduresTechnische Universität MünchenMünchenDeutschland
  3. 3.German Center for Neurodegenerative Diseases (DZNE)BonnDeutschland
  4. 4.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA

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