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EDB-Diff: a EdgeDevice based diffusion network for brain tumor image segmentation

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Abstract

Manually segmenting brain tumor images is time-consuming and not conducive to timely treatment for patients. In recent years, significant progress has been made in the research and development of automated brain tumor image segmentation. CNN-based methods still face instability due to the non-uniqueness of data labels, while diffusion model-based methods have shown significant improvement in stability but are known to have a considerable computational burden. We propose EDB-Diff, a method that has been lightweighted and features a feature separation module based on the analysis of the properties of multi-sequence MRI brain images. Additionally, we have incorporated a broad modality attention mechanism into the denoising network, which enhances the network’s sensitivity to specific features of each sequence without compromising its ability to integrate common features. We evaluated our method on the BraTS2023 dataset, achieving a 60.66% reduction in the number of parameters and a 72.87% increase in inference speed on edge computing devices, while maintaining comparable Dice scores and exhibiting better HD95 stability.

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Funding

These research outcomes were jointly funded by the Guangdong Province Key Field R&D Program project “Research on Key Technologies of Embedded High-Performance Digital Signal Processors (DSP)” (Project No.: 2018B010115002), the Guangdong Province Key Field R&D Program project “Research on Brain-Like Intelligence Key Technologies and Systems” (Project No.: 2018B030338001), the Guangzhou Basic Research Program Basic and Applied Basic Research project “Research on Digital Image Watermarking Technology for Neural Style Transfer” (Project No.: 202201010595), the Guangdong University of Technology Young Hundred Talents Program “Research on Neural Style Transfer and Image Coloring Technology” (Project No.: 220413548), and the 2023 Guangdong University of Technology Undergraduate Teaching Project “Research on the Integration of Programming Practice and Ideological and Political Education in the Basic Information Theory” (Project No.: 211230164).

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Authors and Affiliations

  1. School of Integrated Circuit, Guangdong University of Technology, Guangzhou, China

    Yijun Liu & Wujian Ye

  2. School of Information Engineering, Guangdong University of Technology, Guangzhou, China

    Linfeng Xie

Authors
  1. Yijun Liu
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  2. Linfeng Xie
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  3. Wujian Ye
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Contributions

Y.L propose conceptual models and hypotheses for research and provide experimental resources, L.X conduct data analysis and design experiments, W.Y provide experimental guidance, L.X. and W.Y. wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Wujian Ye.

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Liu, Y., Xie, L. & Ye, W. EDB-Diff: a EdgeDevice based diffusion network for brain tumor image segmentation. Multimedia Systems 30, 342 (2024). https://doi.org/10.1007/s00530-024-01580-w

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