Head and neck tumor segmentation in PET/CT: The HECKTOR challenge

Autor: Valentin Oreiller, Vincent Andrearczyk, Mario Jreige, Sarah Boughdad, Hesham Elhalawani, Joel Castelli, Martin Vallières, Simeng Zhu, Juanying Xie, Ying Peng, Andrei Iantsen, Mathieu Hatt, Yading Yuan, Jun Ma, Xiaoping Yang, Chinmay Rao, Suraj Pai, Kanchan Ghimire, Xue Feng, Mohamed A. Naser, Clifton D. Fuller, Fereshteh Yousefirizi, Arman Rahmim, Huai Chen, Lisheng Wang, John O. Prior, Adrien Depeursinge
Přispěvatelé: Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), University of Applied Sciences of Western Switzerland, Haute École Spécialisée de Suisse Occidentale Valais-Wallis (HES-SO Valais-Wallis), Harvard Medical School [Boston] (HMS), CRLCC Eugène Marquis (CRLCC), Université de Sherbrooke (UdeS), Henry Ford Hospital, Shaanxi Normal University (SNNU), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Icahn School of Medicine at Mount Sinai [New York] (MSSM), Nanjing University of Science and Technology (NJUST), Maastricht University [Maastricht], University of Virginia, The University of Texas M.D. Anderson Cancer Center [Houston], MD Anderson Cancer Center [Houston], The University of Texas Health Science Center at Houston (UTHealth), BC Cancer Agency Research Centre (BCCRC), Shanghai Jiao Tong University [Shanghai], Haute Ecole Spécialisée de Suisse Occidentale (HES-SO), Siemens Healthi-neers Switzerland, Swiss National Science Foundation (SNSF) [205320_179069], Swiss Personalized Health Network (SPHN), Jonchère, Laurent, RS: GROW - R2 - Basic and Translational Cancer Biology
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Medical Image Analysis
Medical Image Analysis, 2022, 77, pp.102336. ⟨10.1016/j.media.2021.102336⟩
Medical Image Analysis, 77:102336. Elsevier
ISSN: 1361-8415
1361-8423
DOI: 10.1016/j.media.2021.102336⟩
Popis: International audience; This paper relates the post-analysis of the first edition of the HEad and neCK TumOR (HECKTOR) challenge. This challenge was held as a satellite event of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020, and was the first of its kind focusing on lesion segmentation in combined FDG-PET and CT image modalities. The challenge's task is the automatic segmentation of the Gross Tumor Volume (GTV) of Head and Neck (HandN) oropharyngeal primary tumors in FDG-PET/CT images. To this end, the participants were given a training set of 201 cases from four different centers and their methods were tested on a held-out set of 53 cases from a fifth center. The methods were ranked according to the Dice Score Coefficient (DSC) averaged across all test cases. An additional inter-observer agreement study was organized to assess the difficulty of the task from a human perspective. 64 teams registered to the challenge, among which 10 provided a paper detailing their approach. The best method obtained an average DSC of 0.7591, showing a large improvement over our proposed baseline method and the inter-observer agreement, associated with DSCs of 0.6610 and 0.61, respectively. The automatic methods proved to successfully leverage the wealth of metabolic and structural properties of combined PET and CT modalities, significantly outperforming human inter-observer agreement level, semi-automatic thresholding based on PET images as well as other single modality-based methods. This promising performance is one step forward towards large-scale radiomics studies in HandN cancer, obviating the need for error-prone and time-consuming manual delineation of GTVs. (C) 2022 The Authors. Published by Elsevier B.V.
Databáze: OpenAIRE
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