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Glioblastoma Survival Prediction

  • Zeina A. ShboulEmail author
  • Mahbubul Alam
  • Lasitha Vidyaratne
  • Linmin Pei
  • Khan M. Iftekharuddin
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

Glioblastoma is a high-grade invasive astrocytoma tumor. The highly invasive nature makes timely detection and characterization of the tumor critical for the survivability prediction of patients. This work proposes MRI- and clinical information-based automated pipeline that implements various state-of-the-art image processing, machine learning, and deep learning techniques to obtain robust tumor segmentation and patient survival estimation. We use 163 cases from the training dataset, and 28 cases from the validation dataset provided by the BraTS 2018 challenge for the evaluation of our model. We achieve an accuracy of 0.679 using the validation dataset and that of 0.519 for the test dataset.

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Notes

Acknowledgements

This work was funded by NIBIB/NIH grant# R01 EB020683.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zeina A. Shboul
    • 1
    Email author
  • Mahbubul Alam
    • 1
  • Lasitha Vidyaratne
    • 1
  • Linmin Pei
    • 1
  • Khan M. Iftekharuddin
    • 1
  1. 1.Vision Lab, Electrical and Computer EngineeringOld Dominion UniversityNorfolkUSA

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