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

Edward Kim, Tian Shen, Xiaolei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

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 faster than conventional CPU methods, enabling user feedback at interactive rates.

Original languageEnglish (US)
Title of host publicationProceedings of the 23rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2010
Pages232-237
Number of pages6
DOIs
StatePublished - 2010
Event23rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2010 - Perth, Australia
Duration: Oct 12 2010Oct 15 2010

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
ISSN (Print)1063-7125

Conference

Conference23rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2010
CountryAustralia
CityPerth
Period10/12/1010/15/10

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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