2022年医学影像计算大会（MICCAI）HECKTOR挑战赛概述：PET/CT中头颈部肿瘤的自动分割与结果预测
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT.
作者信息
Andrearczyk Vincent, Oreiller Valentin, Abobakr Moamen, Akhavanallaf Azadeh, Balermpas Panagiotis, Boughdad Sarah, Capriotti Leo, Castelli Joel, Le Rest Catherine Cheze, Decazes Pierre, Correia Ricardo, El-Habashy Dina, Elhalawani Hesham, Fuller Clifton D, Jreige Mario, Khamis Yornna, La Greca Agustina, Mohamed Abdallah, Naser Mohamed, Prior John O, Ruan Su, Tanadini-Lang Stephanie, Tankyevych Olena, Salimi Yazdan, Vallières Martin, Vera Pierre, Visvikis Dimitris, Wahid Kareem, Zaidi Habib, Hatt Mathieu, Depeursinge Adrien
机构信息
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
出版信息
Head Neck Tumor Chall (2022). 2023;13626:1-30. doi: 10.1007/978-3-031-27420-6_1. Epub 2023 Mar 18.
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. is the fully automatic segmentation of H&N primary Gross Tumor Volume (GTVp) and metastatic lymph nodes (GTVn) from FDG-PET/CT images. 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 () of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.
本文概述了头颈部肿瘤分割与预后预测（HECKTOR）挑战赛的第三版，该挑战赛作为2022年第25届国际医学图像计算与计算机辅助干预会议（MICCAI）的卫星活动举办。该挑战赛包括两项与头颈部癌（H&N）患者的FDG-PET/CT图像自动分析相关的任务，重点是口咽区域。任务1是从FDG-PET/CT图像中全自动分割H&N原发性大体肿瘤体积（GTVp）和转移性淋巴结（GTVn）。任务2是根据相同的FDG-PET/CT和临床数据全自动预测无复发生存期（RFS）。数据从九个中心收集，共883例，包括FDG-PET/CT图像和临床信息，分为524个训练病例和359个测试病例。最佳方法在任务1中获得的聚合骰子相似系数（）为0.788，在任务2中获得的一致性指数（C-index）为0.682。
Zotero 插件