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Segmentation of Vertebral Metastases in MRI Using an U-Net like Convolutional Neural Network

  • Georg HilleEmail author
  • Max Dünnwald
  • Mathias Becker
  • Johannes Steffen
  • Sylvia Saalfeld
  • Klaus Tönnies
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

This study’s objective was to segment vertebral metastases in diagnostic MR images by using a deep learning-based approach. Segmentation of such lesions can present a pivotal step towards enhanced therapy planning and implementation of minimally-invasive interventions like radiofrequency ablations. For this purpose, we used a U-Net-like architecture trained with 38 patient-cases. Our proposed method has been evaluated by comparison to expertly annotated lesion segmentations via Dice coeffcients, sensitivity and specificity rates. While the experiments with T1-weighted MRI images yielded promising results (average Dice score of 73:84 %), T2-weighted images were in average rather insufficient (53:02 %). To our best knowledge, our proposed study is the first to tackle this particular issue, which limits direct comparability with related works. In respect to similar deep learning-based lesion segmentations, e.g. in liver MR images or spinal CT images, our experiments with T1-weighted MR images show similar or in some respects superior segmentation quality.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Georg Hille
    • 1
    Email author
  • Max Dünnwald
    • 1
  • Mathias Becker
    • 2
  • Johannes Steffen
    • 1
  • Sylvia Saalfeld
    • 1
  • Klaus Tönnies
    • 1
  1. 1.Department of and GraphicsUniversity of MagdeburgMagdeburgDeutschland
  2. 2.Department of NeuroradiologyUniversity Hospital of MagdeburgMagdeburgDeutschland

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