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- W4309672242 abstract "Head and neck (H&N) cancers are among the most common cancers worldwide (5th leading cancer by incidence). Accurate segmentation of H&N tumors can improve the early diagnosis rate of cancers for timely treatment. H&N tumor segmentation challenge is the equidensity between the tumor and surrounding tissues, which shows low contrast in CT. In contrast, PET images can reflect the distinction between the lesion region and normal tissue through metabolic activity but show low spatial resolution. With the underlying assumption that each modality contains complementary information, we introduce a novel L2-Norm Scaled Transformer (NSTR) multi-modal segmentation method in PET-CT images. The proposed network comprises the Embedding block, L2-Norm Transformer blocks, 3D Deformable down-sampling blocks, and Feature fusion module, which can fully exploit the high sensitivity of PET images to tumors and the anatomical information of CT images. Our method proposes a powerful 3D fusion network that uses a U-shaped structure to exploit complementary features of different models at multiple scales to increase the cubical representations between different modalities. We conducted a comprehensive experimental analysis on the HECKTOR PET-CT dataset. The results indicated NSTR has powerful featured representation capability and surpasses the state-of-the-art H&N tumor segmentation methods in DSC, Jaccard, RVD, and HD95. (our code will be publicly available soon)." @default.
- W4309672242 created "2022-11-29" @default.
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- W4309672242 date "2022-10-09" @default.
- W4309672242 modified "2023-09-24" @default.
- W4309672242 title "L2-Norm Scaled Transformer for 3D Head and Neck Primary Tumors Segmentation in PET-CT" @default.
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- W4309672242 doi "https://doi.org/10.1109/smc53654.2022.9945335" @default.
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