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A framework combining DNN and level-set method to segment brain tumor in multi-modalities MR image

  • Pinle Qin
  • Jinjing Zhang
  • Jianchao ZengEmail author
  • Haiyan Liu
  • Yuhao Cui
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  • 43 Downloads

Abstract

The magnetic resonance (MR) brain tumor image segmentation can quantitatively analyze the tumor size and provide a large number of brain functional and anatomical information, which to a certain degree can guide the brain disease diagnosis and treatment planning. In this paper, we proposed a framework for brain tumor MR image segmentation combining deep learning and level-set method. First of all, we trained deep neural network (DNN) to classify center pixel of the image patches according to four MR modalities (T1, T1c, T2 and flair) and generated the segmentation result as initialization for level-set method. Secondly, we refine the segmentation results in edema region by level set in flair modality, which compensated for the discontinuity of the DNN segmentation improving the segmentation accuracy. In order to balance tumor patches proportion in datasets increasing them from 2 to 15%, we select patches randomly with a fixed proportion between tumor and normal tissue. Experiments show that the proposed method can effectively overcome discontinuity in segmentation result and obtain a satisfied segmentation results.

Keywords

Deep neural network Brain tumor segmentation Level set Data balance 

Notes

Funding

This study was funded by Nature Science Foundation of Shanxi Province (Funding Number: 2015011045).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animal performed by any of the author.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Big dataNUCTaiyuanPeople’s Republic of China
  2. 2.Collaborative Innovation Center for Molecular ImagingTaiyuanPeople’s Republic of China

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