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Abstract #3771

Deep learning-based auto-segmentation of neck nodal metastases on longitudinal MR images using self-distilled masked image transformer

Jue Jiang1, Ramesh Paudyal1, Bill H. Diplas2, James Han2, Nadeem Riaz2, Vaios Hatzoglou 3, Nancy Lee2, Joseph Deasy 1, Amita Shukla-Dave 1,3, and Harini Veeraraghavan 1
1Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 3Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

Synopsis

Keywords: Segmentation, Cancer

Manual segmentation of normal and tumor tissues on MRI is a traditional approach that is still used, but it is a very challenging and time-consuming method requiring a high level of precision and has shown inter-reader contouring variability. Therefore, semi- or fully automated segmentation algorithms are essential to segment tumors such as neck nodal metastases. The present study aimed to apply the previously developed deep learning-based self-distilled masked image transformer method for auto-segmenting neck nodal metastases on longitudinal T2-weighted MR images.

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Keywords