Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

Volume 7512 of the series Lecture Notes in Computer Science pp 369-376

Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR

  • Darko ZikicAffiliated withMicrosoft Research Cambridge
  • , Ben GlockerAffiliated withMicrosoft Research Cambridge
  • , Ender KonukogluAffiliated withMicrosoft Research Cambridge
  • , Antonio CriminisiAffiliated withMicrosoft Research Cambridge
  • , C. DemiralpAffiliated withBrown University
  • , J. ShottonAffiliated withMicrosoft Research Cambridge
  • , O. M. ThomasAffiliated withCambridge University HospitalsDepartment of Radiology, Cambridge University
  • , T. DasAffiliated withCambridge University Hospitals
  • , R. JenaAffiliated withCambridge University Hospitals
    • , S. J. PriceAffiliated withCambridge University HospitalsDepartment of Clinical Neurosciences, Cambridge University

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We present a method for automatic segmentation of high-grade gliomas and their subregions from multi-channel MR images. Besides segmenting the gross tumor, we also differentiate between active cells, necrotic core, and edema. Our discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest. Our method classifies the individual tissue types simultaneously, which has the potential to simplify the classification task. The approach is computationally efficient and of low model complexity. The validation is performed on a labeled database of 40 multi-channel MR images, including DTI. We assess the effects of using DTI, and varying the amount of training data. Our segmentation results are highly accurate, and compare favorably to the state of the art.