A parallel cellular automata with label priors for interactive brain tumor segmentation

  • Authors:

  • E. Kim; Tian Shen; Xiaolei Huang

  • Affiliations:

  • Dept. of Comput. Sci. & Eng., Lehigh Univ., Bethlehem, PA, USA;Dept. of Comput. Sci. & Eng., Lehigh Univ., Bethlehem, PA, USA;Dept. of Comput. Sci. & Eng., Lehigh Univ., Bethlehem, PA, USA

  • Venue:
  • CBMS '10 Proceedings of the 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems
  • Year:
  • 2010

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Abstract

We present a novel method for 3D brain tumor volume segmentation based on a parallel cellular automata framework. Our method incorporates prior label knowledge gathered from user seed information to influence the cellular automata decision rules. Our proposed method is able to segment brain tumor volumes quickly and accurately using any number of label classifications. Exploiting the inherent parallelism of our algorithm, we adopt this method to the Graphics Processing Unit (GPU). Additionally, we introduce the concept of individual label strength maps to visualize the improvements of our method. As we demonstrate in our quantitative and qualitative results, the key benefits of our system are accuracy, robustness to complex structures, and speed. We compute segmentations nearly 45脳 faster than conventional CPU methods, enabling user feedback at interactive rates.