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Multi-view fusion segmentation for brain glioma on CT images

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Abstract

Computed tomography (CT) images of the brain aid the radiotherapy of glioma. The identification and contouring of the gross tumor volume (GTV) are important for radiotherapy. However, manual segmenting GTV is time-consuming, exhausting, and subjective, and automated methods are desired. To overcome these shortcomings, a novel neural network framework based on multi-view fusion is proposed to segment GTV in brain glioma automatically. The multi-view image that includes the previous image, current image, and following image is inputted in this framework to abstract extra spatial features and then aggregated to segment the GTV. Compared with the 2D segmentation framework, the proposed framework retains more spatial information due to the multi-view image. Meanwhile, compared with the 3D segmentation framework, the proposed framework considers fewer images, which means the model has fewer parameters and is easier to train while retaining much useful spatial information. Moreover, the GliomaCT dataset, a large CT dataset collected from West China Hospital, is used to train, validate, and test the proposed method. The performance of the proposed method and other state-of-the-art methods are compared on this dataset. The high dice similarity coefficient achieved in the experiments demonstrates the effectiveness of the proposed method for segmenting the GTV in brain glioma.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No.2018AAA0100201).

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Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of China

    Han Wang, Junjie Hu, Lei Zhang & Zhang Yi

  2. Department of Radiation Oncology, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China

    Ying Song & Sen Bai

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  1. Han Wang
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  2. Junjie Hu
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  3. Ying Song
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  4. Lei Zhang
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  5. Sen Bai
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Contributions

Han Wang: Writing-original draft, Methodology. Junjie Hu: Writing-review & editing. Ying Song: Writing-review & editing. Lei Zhang: Supervision. Sen Bai: Supervision, Project administration. Zhang Yi: Supervision, Project administration, Funding acquisition, Writing-review & editing

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Correspondence to Sen Bai or Zhang Yi.

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Cite this article

Wang, H., Hu, J., Song, Y. et al. Multi-view fusion segmentation for brain glioma on CT images. Appl Intell 52, 7890–7904 (2022). https://doi.org/10.1007/s10489-021-02784-7

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