A multichannel Markov random field approach for automated segmentation of breast cancer tumor in DCE-MRI data using kinetic observation model

  • Authors:

  • Ahmed B. Ashraf;Sara Gavenonis;Dania Daye;Carolyn Mies;Michael Feldman;Mark Rosen;Despina Kontos

  • Affiliations:

  • The University of Pennsylvania, Philadelphia, PA;The University of Pennsylvania, Philadelphia, PA;The University of Pennsylvania, Philadelphia, PA;The University of Pennsylvania, Philadelphia, PA;The University of Pennsylvania, Philadelphia, PA;The University of Pennsylvania, Philadelphia, PA;The University of Pennsylvania, Philadelphia, PA

  • Venue:
  • MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
  • Year:
  • 2011

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Abstract

We present a multichannel extension of Markov random fields (MRFs) for incorporating multiple feature streams in the MRF model. We prove that for making inference queries, any multichannel MRF can be reduced to a single channel MRF provided features in different channels are conditionally independent given the hidden variable. Using this result we incorporate kinetic feature maps derived from breast DCE MRI into the observation model of MRF for tumor segmentation. Our algorithm achieves an ROC AUC of 0.97 for tumor segmentation. We present a comparison against the commonly used approach of fuzzy C-means (FCM) and the more recent method of running FCM on enhancement variance features (FCM-VES). These previous methods give a lower AUC of 0.86 and 0.60 respectively, indicating the superiority of our algorithm. Finally, we investigate the effect of superior segmentation on predicting breast cancer recurrence using kinetic DCE MRI features from the segmented tumor regions. A linear prediction model shows significant prediction improvement when segmenting the tumor using the proposed method, yielding a correlation coefficient r = 0.78 (p