Abstract
Coronavirus Disease 2019 (COVID-19) ravaged the world in early 2020, causing great harm to human health. However, there are several challenges to segment the infected areas from computed tomography (CT) image, including blurry boundaries between the lesion and normal lung tissues, and uncertain characteristics about lesion’s scale, location, and texture. To solve these problems, a COVID-19 lesion segmentation network (LS-Net) based on probabilistic diffusion model is proposed to segment lesion areas from CT images. The feature fusion decoder module is introduced to aggregate high-level features and generate a guidance as the next steps so that the small lesion could not be omitted. In addition, the attention mechanism is set to pay attention to the information about position of lesion’s edge. So, the LS-Net framework can improve the precision of lesion segmentation result from CT image slice. Experiments on datasets such as the COVID-19 CT Segmentation dataset shows that LS-Net is advanced than most current segmentation models.
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Shi, A. et al. (2024). LS-Net: COVID-19 Lesion Segmentation from CT Image via Diffusion Probabilistic Model. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14498. Springer, Cham. https://doi.org/10.1007/978-3-031-50078-7_13
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