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Glioma Prognosis: Segmentation of the Tumor and Survival Prediction Using Shape, Geometric and Clinical Information

  • Mobarakol Islam
  • V. Jeya Maria Jose
  • Hongliang RenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

Segmentation of brain tumor from magnetic resonance imaging (MRI) is a vital process to improve diagnosis, treatment planning and to study the difference between subjects with tumor and healthy subjects. In this paper, we exploit a convolutional neural network (CNN) with hypercolumn technique to segment tumor from healthy brain tissue. Hypercolumn is the concatenation of a set of vectors which form by extracting convolutional features from multiple layers. Proposed model integrates batch normalization (BN) approach with hypercolumn. BN layers help to alleviate the internal covariate shift during stochastic gradient descent (SGD) training by zero-mean and unit variance of each mini-batch. Survival Prediction is done by first extracting features (Geometric, Fractal, and Histogram) from the segmented brain tumor data. Then, the number of days of overall survival is predicted by implementing regression on the extracted features using an artificial neural network (ANN). Our model achieves a mean dice score of 89.78%, 82.53% and 76.54% for the whole tumor, tumor core and enhancing tumor respectively in segmentation task and 67.9% in overall survival prediction task with the validation set of BraTS 2018 challenge. It obtains a mean dice accuracy of 87.315%, 77.04% and 70.22% for the whole tumor, tumor core and enhancing tumor respectively in the segmentation task and a 46.8% in overall survival prediction task in the BraTS 2018 test data set.

Keywords

Brain tumor segmentation Glioma Convolutional neural network Hypercolumn PixelNet Magnetic resonance imaging Survival prediction 

Notes

Acknowledgement

This work is supported by the Singapore Academic Research Fund under Grant R-397-000-227-112, NUSRI China Jiangsu Provincial Grant BK20150386 and BE2016077 and NMRC Bedside & Bench under grant R-397-000-245-511 awarded to Dr. Hongliang Ren.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mobarakol Islam
    • 1
    • 2
  • V. Jeya Maria Jose
    • 2
    • 3
  • Hongliang Ren
    • 2
    Email author
  1. 1.NUS Graduate School for Integrative Sciences and Engineering (NGS)National University of SingaporeSingaporeSingapore
  2. 2.Department of Biomedical EngineeringNational University of SingaporeSingaporeSingapore
  3. 3.Department of Instrumentation and Control EngineeringNational Institute of TechnologyTiruchirappalliIndia

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