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Attention-Driven Deep Learning for Pathological Spine Segmentation

  • Anjany SekuboyinaEmail author
  • Jan Kukačka
  • Jan S. Kirschke
  • Bjoern H. Menze
  • Alexander Valentinitsch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10734)

Abstract

Accurate segmentation of the spine in computed tomography (CT) images is mandatory for quantitative analysis, e.g. in osteoporosis, but remains challenging due to high variability in vertebral morphology and spinal anatomy among patients. Conventionally, spine segmentation was performed by model-based techniques employing spine atlases or statistical shape models. We argue that such approaches, even though intuitive, fail to address clinical abnormalities such as vertebral fractures, scoliosis, etc. We propose a novel deep learning-based method for segmenting the spine, which does not rely on any pre-defined shape model. We employ two networks: one for localisation and another for segmentation. Since a typical spine CT scan cannot be processed at once owing to its large dimensions, we find that both nets are essential to work towards a perfect segmentation. We evaluate our framework on three datasets containing healthy and fractured cases: two private and one public. Our approach achieves a mean Dice coefficient of \({\sim }0.87\), which is comparable but not higher than the state-of-art model-based approaches. However, we show that our approach handles degenerate cases more accurately.

Keywords

Spine segmentation Automated segmentation Deep learning Fully convolutional network 

Notes

Acknowledgements

This work was funded from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (GA637164–iBack–ERC-2014-STG).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Anjany Sekuboyina
    • 1
    • 2
    Email author
  • Jan Kukačka
    • 1
    • 2
  • Jan S. Kirschke
    • 2
  • Bjoern H. Menze
    • 1
  • Alexander Valentinitsch
    • 1
    • 2
  1. 1.Department of InformaticsTechnische Universität MünchenMunichGermany
  2. 2.Department of Diagnostic and Interventional NeuroradiologyKlinikum rechts der IsarMunichGermany

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