Deep learning-based techniques for the automatic segmentation of organs in thoracic computed tomography images: A Comparative study
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IEEE
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http://xplorestaging.ieee.org/ielx7/9395728/9395748/09396016.pdf?arnumber=9396016
Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Image encryption of medical images;Advances in Computers;2024
2. HCIU: Hybrid clustered inception‐based UNET for the automatic segmentation of organs at risk in thoracic computed tomography images;International Journal of Imaging Systems and Technology;2023-07-31
3. A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images;Medical & Biological Engineering & Computing;2023-05-30
4. Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images Using Ensembled U-Net InceptionV3 Model;Journal of Computational Biology;2023-03-01
5. An approach to remove extraneous slices from CT for kidney segmentation;2022 1st IEEE International Conference on Industrial Electronics: Developments & Applications (ICIDeA);2022-10-15
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