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564-06-006
ISMRM Abstract
Cross-Domain Transfer Learning and Comparative Analysis of U-Net Architectures for Head and Neck Tumor Segmentation
Accepted
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2. Oreiller V, Andrearczyk V, Jreige M, et al. Head and neck tumor segmentation in PET/CT: The HECKTOR challenge. Med Image Anal. 2022;77:102336. doi:10.1016/j.media.2021.102336 [doi]
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8. Training Dataset for HNTSMRG 2024 Challenge [DOI |10.5281/zenodo.11199558]
9. Bertels, J. et al. (2019). Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol11765. Springer, Cham.
10. Eelbode, T., Bertels, J., Berman, M., Vandermeulen, D., Maes, F., Bisschops, R. and Blaschko, M.B., 2020. Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index. IEEE transactions on medical imaging, 39(11), pp.3679-3690.
11. Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J. and Maier-Hein, K.H., 2021. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), pp.203-211.
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http://echo.ismrm.org/p/ISMRM2026/564-06-006