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Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

  • Po-Yu KaoEmail author
  • Thuyen Ngo
  • Angela Zhang
  • Jefferson W. Chen
  • B. S. ManjunathEmail author
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1 mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.

Keywords

Brain tumor segmentation Brain parcellation Group normalization Hard negative mining Ensemble modeling Overall survival prediction Tractographic feature 
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Notes

Acknowledgements

This research was partially supported by a National Institutes of Health (NIH) award # 5R01NS103774-02.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Vision Research LabUniversity of CaliforniaSanta BarbaraUSA
  2. 2.UC Irvine HealthUniversity of CaliforniaIrvineUSA

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