Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge

Overview

Journal ArXiv

Date 2024 Dec 9

PMID 39650598

Authors

Kareem A Wahid

Cem Dede

Dina M El-Habashy

Serageldin Kamel

Michael K Rooney

Yomna Khamis

Moamen R A Abdelaal

Sara Ahmed

Kelsey L Corrigan

Enoch Chang

Stephanie O Dudzinski

Travis C Salzillo

Brigid A McDonald

Samuel L Mulder

Lucas McCullum

Qusai Alakayleh

Carlos Sjogreen

Renjie He

Abdallah S R Mohamed

Stephen Y Lai

John P Christodouleas

Andrew J Schaefer

Mohamed A Naser

Clifton D Fuller

Affiliations

Soon will be listed here.

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 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 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.

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