Kareem Wahid

Abstract: Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in AI-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge. This challenge addresses the scarcity of large, publicly available AI-ready adaptive RT datasets in HNC and explores the potential of incorporating multi-timepoint data to enhance RT auto-segmentation performance. Participants tackled two HNC segmentation tasks: automatic delineation of primary gross tumor volume and gross metastatic regional lymph nodes on pre-RT (Task 1) and mid-RT (Task 2) T2-weighted scans. This talk highlights the development and outcomes of this large-scale data challenge, which attracted international attention, drawing over 100 registrations and active participation from 19 teams.