Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge
Book Title
Head and Neck Tumor Segmentation for MR-Guided Applications
Files
Editors
Wahid KA, Dede C, Naser MA, Fuller CD
Description
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 artificial intelligence (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, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. 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 (GTVp) and gross metastatic regional lymph nodes (GTVn) on pre-RT (Task 1) and mid-RT (Task 2) T2-weighted scans. The challenge provided 150 HNC cases for training and 50 for final testing hosted on grand-challenge.org using a Docker submission framework. In total, 19 independent teams from across the world qualified by submitting both their algorithms and corresponding papers, resulting in 18 submissions for Task 1 and 15 submissions for Task 2. Evaluation using the mean aggregated Dice Similarity Coefficient showed top-performing AI methods achieved scores of 0.825 in Task 1 and 0.733 in Task 2. These results surpassed clinician interobserver variability benchmarks, marking significant strides in automated tumor segmentation for MR-guided RT applications in HNC.
First Page
1
Last Page
35
ISBN
9783031832741
Publication Date
5-3-2025
Publisher
Springer
City
Cham
Disciplines
Oncology | Radiology
Recommended Citation
Wahid KA, Dede C, El-Habashy DM, Kamel S, Rooney MK, Khamis Y et al. Overview of the head and neck tumor segmentation for magnetic resonance guided applications (hnts-mrg) 2024 challenge. In: Wahid KA, Dede C, Naser MA, Fuller CD, editors. Head and neck tumor segmentation for mr-guided applications. Springer: Cham; 2025. p.1-35.
