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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