Augmenting Auto-context with Global Geometric Features for Spinal Cord Segmentation

  • Jeremy Kawahara
  • Chris McIntosh
  • Roger Tam
  • Ghassan Hamarneh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)


Anatomical shape variations are typically difficult to model and parametric or hand-crafted models can lead to ill-fitting segmentations. This difficulty can be addressed with a framework like auto-context, that learns to jointly detect and regularize a segmentation. However, mis-segmentation can still occur when a desired structure, such as the spinal cord, has few locally distinct features. High-level knowledge at a global scale (e.g. an MRI contains a single connected spinal cord) is needed to regularize these candidate segmentations. To encode high-level knowledge, we propose to augment the auto-context framework with global geometric features extracted from the detected candidate shapes. Our classifier then learns these high-level rules and rejects falsely detected shapes. To validate our method we segment the spinal cords from 20 MRI volumes composed of patients with and without multiple sclerosis and demonstrate improvements in accuracy, speed, and manual effort required when compared to state-of-the-art methods.


Class Label Principal Component Analysis Model Ground Truth Segmentation Decision Forest Candidate Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Jeremy Kawahara
    • 1
  • Chris McIntosh
    • 1
    • 2
  • Roger Tam
    • 3
  • Ghassan Hamarneh
    • 1
  1. 1.Medical Image Analysis Lab.Simon Fraser UniversityBurnabyCanada
  2. 2.Princess Margaret Cancer CentreUniversity Health NetworkTorontoCanada
  3. 3.MS/MRI Research GroupUniversity of British ColumbiaVancouverCanada

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