Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT.

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Serval ID
serval:BIB_238771614D11
Type
Article: article from journal or magazin.
Collection
Publications
Institution
UNIL/CHUV
Title
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT.
Journal
Head and neck tumor segmentation and outcome prediction
Author(s)
Andrearczyk V., Oreiller V., Abobakr M., Akhavanallaf A., Balermpas P., Boughdad S., Capriotti L., Castelli J., Le Rest C.C., Decazes P., Correia R., El-Habashy D., Elhalawani H., Fuller C.D., Jreige M., Khamis Y., La Greca A., Mohamed A., Naser M., Prior J.O., Ruan S., Tanadini-Lang S., Tankyevych O., Salimi Y., Vallières M., Vera P., Visvikis D., Wahid K., Zaidi H., Hatt M., Depeursinge A.
Publication state
Published
Issued date
2023
Peer-reviewed
Oui
Volume
13626
Pages
1-30
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
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 (DSC <sub>agg</sub> ) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.
Keywords
Challenge, Deep learning, Head and neck cancer, Machine learning, Medical imaging, Radiomics, Segmentation
OAI-PMH
oai:serval.unil.ch:BIB_238771614D11
DOI
10.1007/978-3-031-27420-6_1
Pubmed
37195050
Create date
23/05/2023 14:19
Last modification date
19/07/2023 6:55
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