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Enhanced Graph Cuts for Brain Tumor Segmentation Using Bayesian Optimization

  • Mauricio CastañoEmail author
  • Hernán F. GarcíaEmail author
  • Gloria L. Porras-HurtadoEmail author
  • Álvaro A. OrozcoEmail author
  • Jorge I. Marin-HurtadoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

Brain tumor segmentation is a difficult task, due to the shape variability that malignancy brain structures exhibit between patients. The main problem in this process is that the tumor contour is usually computed from parametric models that need to be well-tuned to perform an accurate segmentation. In this paper, we propose an enhanced Graph cut on which the model parameters are selected through a probabilistic approach. Here, we use Bayesian optimization to find the optimal hyperparameters that segment the tumor volume accurately. The experimental results show that by using Bayesian optimization, the graph cut model performs an accurate segmentation over brain volumes in comparison with common segmentation methods in the state-of-the-art.

Keywords

Bayesian optimization Graph cuts Gaussian processes Brain tumor segmentation 

Notes

Acknowledgments

This research is developed under the project financed by COLCIENCIAS with code 111074455860. H.F. García is funded by Colciencias under the program: Formación de alto nivel para la ciencia, la tecnología y la innovación - Convocatoria 617 de 2013.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.GAMMA Research Group, Universidad del QuindíoArmeniaColombia
  2. 2.Grupo de Investigación Salud Comfamiliar, Comfamiliar RisaraldaPereiraColombia
  3. 3.Grupo de Investigación en Automática, Universidad Tecnológica de PereiraPereiraColombia
  4. 4.GDSPROC Research Group, Universidad del QuindíoArmeniaColombia

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