Tao Zhou1,3, Yunfeng Pan1,3,*, Huiling Lu2, Pei Dang1,3, Yujie Guo1,3, Yaxing Wang1,3
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4813-4832, 2024, DOI:10.32604/cmc.2024.054685
- 12 September 2024
Abstract Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion. Such as Positron Emission Computed Tomography (PET), Computed Tomography (CT), and PET-CT. How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions. To solve the problem, the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network (Guide-YNet) is proposed in this paper. Firstly, a double-encoder single-decoder U-Net is used as the backbone in this model, a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and… More >
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