[Fully Automatic Glioma Segmentation Algorithm of Magnetic Resonance Imaging Based on 3D-UNet With More Global Contextual Feature Extraction: An Improvement on Insufficient Extraction of Global Features].

Journal: Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
Published Date: Mar 20, 2024

Abstract

OBJECTIVE: The fully automatic segmentation of glioma and its subregions is fundamental for computer-aided clinical diagnosis of tumors. In the segmentation process of brain magnetic resonance imaging (MRI), convolutional neural networks with small convolutional kernels can only capture local features and are ineffective at integrating global features, which narrows the receptive field and leads to insufficient segmentation accuracy. This study aims to use dilated convolution to address the problem of inadequate global feature extraction in 3D-UNet.

Authors

  • Hengyi Tian
    （ 100048） School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
  • Yu Wang
    Clinical and Technical Support, Philips Healthcare, Shanghai, China.
  • Yarong Ji
    Beijing Key Laboratory of Big Data Technology for Food Safety, School of Artificial Intelligence, Beijing Technology and Business University, Beijing, 100048, China.
  • Md Mostafizur Rahman
    （ 100048） School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.

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