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Automatic Brain Tumor Segmentation and Overall Survival Prediction Using Machine Learning Algorithms

  • Eric Carver
  • Chang Liu
  • Weiwei Zong
  • Zhenzhen Dai
  • James M. Snyder
  • Joon Lee
  • Ning WenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

Purpose: This study was designed to evaluate the ability of a U-net neural net-work to properly identify three regions of a brain tumor and an ELM for the prediction of patient overall survival after gross tumor resection using preoperative MR images.

Methods: 210 GBM patients were used for training, while 66 LGG and GBM patients were used for validation. Multiple preprocessing steps were performed on each patient’s data before loading them into the model. The segmentation model consists of three different U-nets, one for each region of interest. These created segmentations were then analyzed by use of common quantitative metrics with respect to physician created contours. Regarding the patient overall survival prediction, 59 high grade glioma patients with gross total resection (GTR) were provided for training. 28 patients with GTR were used to validate the algorithm.

Results: The average [s.d] DSC for the whole tumor, enhanced tumor, and tumor core contours were 0.882 [0.080], 0.712 [0.294], and 0.769 [0.263], respectively. The average [s.d.] Hausdorff distance were 7.09 [11.57], 4.46 [8.32], and 9.57 [14.08], respectively. The average [s.d.] sensitivity for the whole tumor, enhanced tumor, and tumor core contours were 0.887 [0.126], 0.770 [0.245], and 0.750 [0.293], respectively. The average [s.d.] specificity was 0. 993 [0.005], 0.998 [0.003], 0.998 [0.002], respectively. The predictive power of patient overall survival is 0.607 using an extreme learning machine algorithm.

Conclusion: The U-Net model was very effective in determining accurate location of the whole tumor and segmenting the whole tumor, enhancing tumor and tumor core. The most predictive features of patient overall survival are both age and location of the tumor when all 163 validation cases were utilized.

Keywords

Magnetic resonance imaging Neural network U-net 

Notes

Acknowledgement

This study was supported by a Research Scholar Grant, RSG-15-137-01-CCE from the American Cancer Society.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eric Carver
    • 1
    • 3
  • Chang Liu
    • 1
  • Weiwei Zong
    • 1
  • Zhenzhen Dai
    • 1
  • James M. Snyder
    • 2
  • Joon Lee
    • 1
  • Ning Wen
    • 1
    Email author
  1. 1.Department of Radiation OncologyHenry Ford Health SystemDetroitUSA
  2. 2.Department of NeurosurgeryHenry Ford Health SystemDetroitUSA
  3. 3.Department of OncologyWayne State UniversityDetroitUSA

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