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Automatic segmentation of brain glioma based on XY-Net

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Abstract

Glioma is a malignant primary brain tumor, which can easily lead to death if it is not detected in time. Magnetic resonance imaging is the most commonly used technique to diagnose gliomas, and precise outlining of tumor areas from magnetic resonance images (MRIs) is an important aid to physicians in understanding the patient’s condition and formulating treatment plans. However, relying on radiologists to manually depict tumors is a tedious and laborious task, so it is clinically important to investigate an automated method for outlining glioma regions in MRIs. To liberate radiologists from the heavy task of outlining tumors, we propose a fully convolutional network, XY-Net, based on the most popular U-Net symmetric encoder-decoder structure to perform automatic segmentation of gliomas. We construct two symmetric sub-encoders for XY-Net and build interconnected X-shaped feature map transmission paths between the sub-encoders, while maintaining the feature map concatenation between each sub-encoder and the decoder. Moreover, a loss function composed of the balanced cross-entropy loss function and the dice loss function is used in the training task of XY-Net to solve the class unevenness problem of the medical image segmentation task. The experimental results show that the proposed XY-Net has a 2.16% improvement in dice coefficient (DC) compared to the network model with a single encoder structure, and compare with some state-of-the-art image segmentation methods, XY-Net achieves the best performance. The DC, HD, recall, and precision of our method on the test set are 74.49%, 10.89 mm, 78.06%, and 76.30%, respectively. The combination of sub-encoders and cross-transmission paths enables the model to perform better; based on this combination, the XY-Net achieves an end-to-end automatic segmentation of gliomas on 2D slices of MRIs, which can play a certain auxiliary role for doctors in grasping the state of illness.

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  1. https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html

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Funding

This study was supported by the Funding of the National Natural Science Foundation of China (Grant No. 61863027), Jiangxi Provincial Natural Science Foundation (Grant No. 20202BABL206112), and the Key Research and Development Plan of Jiangxi Province (Grant No. 20202BBGL73057).

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Authors and Affiliations

  1. Nanchang Key Laboratory of Medical and Technology Research, Nanchang University, Nanchang, 330006, China

    Wenbin Xu & Jizhong Liu

  2. Department of Radiology, Jiangxi Provincial People’s Hospital, the First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, China

    Bing Fan

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  1. Wenbin Xu
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Contributions

Wenbin Xu carried out data collection, experimental design, and manuscript drafting. Jizhong Liu and Bing Fan participated in the data collection, helped to design the work, and revised the final version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jizhong Liu or Bing Fan.

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Ethical approval for this investigation was obtained from the Research Ethics Committee of Jiangxi Provincial People’s Hospital, and the reference number was 2019–051.

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Xu, W., Liu, J. & Fan, B. Automatic segmentation of brain glioma based on XY-Net. Med Biol Eng Comput 62, 153–166 (2024). https://doi.org/10.1007/s11517-023-02927-7

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