Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; Department of Nuclear Medicine and Molecular Imaging, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland.
Med Image Anal. 2022 Apr;77:102336. doi: 10.1016/j.media.2021.102336. Epub 2021 Dec 25.
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 (H&N) 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 H&N cancer, obviating the need for error-prone and time-consuming manual delineation of GTVs.
本文对首届 HEad and neCK TumOR（HECKTOR）挑战赛的第一版进行了后分析。该挑战赛作为 2020 年第 23 届国际医学影像计算与计算机辅助干预会议（MICCAI）的卫星会议举行，是首个专注于 FDG-PET 和 CT 图像模态联合病变分割的挑战赛。挑战赛的任务是自动分割头颈部（H&N）口咽原发肿瘤的 FDG-PET/CT 图像的大体肿瘤体积（GTV）。为此，参与者获得了来自四个不同中心的 201 个病例的训练集，并在来自第五个中心的 53 个病例的验证集上测试了他们的方法。根据在所有测试病例上的平均骰子相似系数（DSC）对方法进行了排名。还组织了一项额外的观察者间一致性研究，从人类的角度评估任务的难度。有 64 个团队注册参加了挑战赛，其中 10 个团队提供了详细介绍其方法的论文。最佳方法的平均 DSC 为 0.7591，与我们提出的基线方法和观察者间一致性（分别为 0.6610 和 0.61）相比，有了很大的提高。自动方法成功地利用了 FDG-PET 和 CT 联合模态的丰富代谢和结构特性，显著优于观察者间一致性水平、基于 PET 图像的半自动阈值分割以及其他基于单一模态的方法。这种有前景的性能是朝着在 H&N 癌症中进行大规模放射组学研究迈出的一步，避免了对 GTV 进行易出错且耗时的手动勾画的需要。
