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Brain Tumor Segmentation Based on Improved Convolutional Neural Network in Combination with Non-quantifiable Local Texture Feature

  • Wu Deng
  • Qinke Shi
  • Kai Luo
  • Yi Yang
  • Ning NingEmail author
Image & Signal Processing
Part of the following topical collections:
  1. Distributed Analytics and Deep Learning in Health Care

Abstract

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis. According to deep learning model, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNN) and dense micro-block difference feature (DMDF) into a unified framework so as to obtain segmentation results with appearance and spatial consistency. Firstly, we propose a local feature to describe the rotation invariant property of the texture. In order to deal with the change of rotation and scale in texture image, Fisher vector encoding method is used to analyze the texture feature, which can combine with the scale information without increasing the dimension of the local feature. The obtained local features have strong robustness to rotation and gray intensity variation. Then, the non-quantifiable local feature is fused to the FCNN to perform fine boundary segmentation. Since brain tumors occupy a small portion of the image, deconvolutional layers are designed with skip connections to obtain a high quality feature map. Compared with the traditional MRI brain tumor segmentation methods, the experimental results show that the segmentation accuracy and stability has been greatly improved. Average Dice index can be up to 90.98%. And the proposed method has very high real-time performance, where brain tumor image can segment within 1 s.

Keywords

Tumor segmentation Convolutional neural network Non-quantifiable local feature;dense micro-block difference Rotation invariant 

Notes

Compliance with Ethical Standards

We declare that we have no conflict of interest. The paper does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Wu Deng
    • 1
  • Qinke Shi
    • 1
  • Kai Luo
    • 1
  • Yi Yang
    • 1
  • Ning Ning
    • 2
    Email author
  1. 1.Information CenterWest China Hospital of Sichuan universityChengduChina
  2. 2.Department of OrthopaedicsWest China Hospital of Sichuan UniversityChengduChina

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