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Brain Tumor Segmentation Using Bit-plane and UNET

  • Tran Anh TuanEmail author
  • Tran Anh Tuan
  • Pham The Bao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

The extraction of brain tumor tissues in 3D Brain Magnetic Resonance Imaging plays an important role in diagnosis gliomas. In this paper, we use clinical data to develop an approach to segment Enhancing Tumor, Tumor Core, and Whole Tumor which are the sub-regions of glioma. Our proposed method starts with Bit-plane to get the most significant and least significant bits which can cluster and generate more images. Then U-Net, a popular CNN model for object segmentation, is applied to segment all of the glioma regions. In the process, U-Net is implemented by multiple kernels to acquire more accurate results. We evaluated the proposed method with the database BRATS challenge in 2018. On validation data, the method achieves a performance of 82%, 68%, and 70% Dice scores and of 77%, 48%, and 51% on testing data for the Whole Tumor, Enhancing Tumor, and Tumor Core respectively.

Keywords

3D brain MRI Brain tumor Bit-plane 2D U-Net CNN BRATS challenge in 2018 

Notes

Acknowledgement

We would like to thank Business Intelligence LAB at University of Economics and Law for supporting us throughout this paper. The study was supported by Science and Technology Incubator Youth Program, managed by the Center for Science and Technology Development, Ho Chi Minh Communist Youth Union, 2018.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Math and Computer ScienceUniversity of Science, Vietnam National UniversityHo Chi Minh CityVietnam
  2. 2.Department of Computer ScienceSai Gon UniversityHo Chi Minh CityVietnam

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