Faculty, Staff and Student Publications
Publication Date
1-1-2023
Journal
Head and Neck Tumor Segmentation and Outcome Prediction
DOI
10.1007/978-3-031-27420-6_1
PMID
37195050
PMCID
PMC10171217
PubMedCentral® Posted Date
3-18-2024
PubMedCentral® Full Text Version
Author MSS
Abstract
This paper presents an overview of the third edition of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge, organized as a satellite event of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. The challenge comprises two tasks related to the automatic analysis of FDG-PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the fully automatic segmentation of H&N primary Gross Tumor Volume (GTVp) and metastatic lymph nodes (GTVn) from FDG-PET/CT images. Task 2 is the fully automatic prediction of Recurrence-Free Survival (RFS) from the same FDG-PET/CT and clinical data. The data were collected from nine centers for a total of 883 cases consisting of FDG-PET/CT images and clinical information, split into 524 training and 359 test cases. The best methods obtained an aggregated Dice Similarity Coefficient (DSCagg) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.
Keywords
Challenge, Medical imaging, Head and neck cancer, Segmentation, Radiomics, Deep learning, Machine learning
Published Open-Access
yes
Recommended Citation
Andrearczyk, Vincent; Oreiller, Valentin; Abobakr, Moamen; et al., "Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT" (2023). Faculty, Staff and Student Publications. 3134.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/3134
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