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

Head and neck tumor segmentation and outcome prediction : third challenge, HECKTOR 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Head and Neck Tumor Segmentation Challenge (3rd : 2022 : Singapor... Pub Date : 2023-01-01 Epub Date: 2023-03-18 DOI:10.1007/978-3-031-27420-6_1

Vincent Andrearczyk, Valentin Oreiller, Moamen Abobakr, Azadeh Akhavanallaf, Panagiotis Balermpas, Sarah Boughdad, Leo Capriotti, Joel Castelli, Catherine Cheze Le Rest, Pierre Decazes, Ricardo Correia, Dina El-Habashy, Hesham Elhalawani, Clifton D Fuller, Mario Jreige, Yornna Khamis, Agustina La Greca, Abdallah Mohamed, Mohamed Naser, John O Prior, Su Ruan, Stephanie Tanadini-Lang, Olena Tankyevych, Yazdan Salimi, Martin Vallières, Pierre Vera, Dimitris Visvikis, Kareem Wahid, Habib Zaidi, Mathieu Hatt, Adrien Depeursinge

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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_agg) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.

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2022 年 MICCAI 上的 HECKTOR 挑战赛概述：PET/CT 中的头颈部肿瘤自动分割和结果预测。

本文概述了第三届头颈部肿瘤分割和结果预测（HECKTOR）挑战赛的情况，该挑战赛是 2022 年第 25 届国际医学影像计算和计算机辅助干预会议（MICCAI）的一项卫星活动。挑战赛包括两项任务，涉及对头颈部癌症（H&N）患者的 FDG-PET/CT 图像进行自动分析，重点是口咽部区域。任务 1 是根据 FDG-PET/CT 图像全自动分割 H&N 原发肿瘤总体积（GTVp）和转移淋巴结（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率（RFS）。数据收集自九个中心，共计 883 个病例，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 中获得了 0.788 的综合骰子相似系数（DSCagg），在任务 2 中获得了 0.682 的一致性指数（C-index）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Head and neck tumor segmentation and outcome prediction : third challenge, HECKTOR 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Head and Neck Tumor Segmentation Challenge (3rd : 2022 : Singapor...

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