The video of the scientific session at MICCAI 2022 is available on the Pathable platform:
Following the success of the first two editions of the HECKTOR challenge in 2020 and 2021, this
challenge will be presented at the 25th International Conference on
Medical Image Computing and Computer Assisted Intervention (MICCAI)
2022. Two tasks are proposed this year
(participants can choose to participate in either or both tasks):
- Task 1: The automatic segmentation of Head and Neck (H&N) primary tumors and lymph nodes (new!) in FDG-PET/CT images;
- Task 2: The prediction of patient outcomes, namely Recurrence-Free Survival (RFS) from the FDG-PET/CT images and available clinical data.
For the 2022 third edition of HECKTOR, we propose expanding the scope of the challenge even further by:
- Adding the task of H&N nodal Gross Tumor Volumes (GTVn) segmentation (which also carries information on the outcome via the presence and amount of metastatic lesions);
- Adding more data with an approximative total of 882 cases (versus 325 in 2021), including three new centers. The test data of 2021 is moved to the training set of 2022, whereas new data from new centers are split between training and test sets;
- Not providing bounding boxes. Only the entire PET/CT images are provided to the challengers for fully automatic algorithms that can perform predictions from entire images;
- Not providing test ground truth tumor delineations for outcome prediction, as we learned from 2021’s results that these delineations were not necessarily needed to achieve best prediction performance.
By focusing on metabolic and morphological tissue properties respectively, PET and CT modalities include complementary and synergistic information for cancerous lesion segmentation as well as tumor characteristics potentially relevant for patient outcome prediction, in addition to usual clinical variables (e.g. age, gender, treatment modality). Modern image analysis methods must be developed and more importantly, rigorously evaluated, in order to extract and leverage this information. The data used in this challenge is multi-centric (9 centers in total), including four centers in Canada [Vallières et al. 2017], two centers in Switzerland [Castelli et al. 2017; Bogowicz et al. 2017], two centers in France [Hatt et al. 2019; Legot et al. 2018], and one center in USA [Ger et al. 2019] for a total of XXX patients with annotated GTVp and GTVn.
[Andrearczyk et al. 2022] Andrearczyk V, et al. "Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images", in: Head and Neck Tumor Segmentation and Outcome Prediction, 1-37 (2022).
[Blanc-Durand et al. 2018] Blanc-Durand P, et al. "Automatic lesion detection and segmentation of 18F-FET PET in gliomas: a full 3D U-Net convolutional neural network study." PLoS One 13.4 (2018): e0195798.
[Bogowicz et al. 2017] Bogowicz M, et al. "Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma." Acta Oncologica 56.11 (2017): 1531-1536.
[Castelli et al. 2017] Castelli J, et al. "A PET-based nomogram for oropharyngeal cancers." European Journal of Cancer 75 (2017): 222-230.
[Chajon et al. 2013] Chajon E, et al. "Salivary gland-sparing other than parotid-sparing in definitive head-and-neck intensity-modulated radiotherapy does not seem to jeopardize local control." Radiation Oncology 8.1 (2013): 1-9.
[Ger et al. 2019] Ger RB, et al. "Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients." PLOS One 14.9 (2019): e0222509
[Hatt et al. 2009] Hatt M, et al. "A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET." IEEE Transactions on Medical Imaging 28.6 (2009): 881-893.
[Heiman and Meinzer 2009] Heimann T, and Meinzer H-P. "Statistical shape models for 3D medical image segmentation: a review." Medical Image Analysis 13.4 (2009): 543-563.
[Legot et al. 2018] Legot F, et al. "Use of baseline 18F-FDG PET scan to identify initial sub-volumes with local failure after concomitant radio-chemotherapy in head and neck cancer." Oncotarget 9.31 (2018): 21811.
[Menze et al. 2014] Menze BH, et al. "The multimodal brain tumor image segmentation benchmark (BRATS)." IEEE Transactions on Medical Imaging 34.10 (2014): 1993-2024.
[Moe et al. 2019] Moe YM, et al. “Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers.” Medical Imaging with Deep Learning (2019).
[Oreiller et al. 2022] Oreiller V., et al., "Head and Neck Tumor Segmentation in PET/CT: The HECKTOR Challenge", Medical Image Analysis, 77:102336 (2022).
[Parkin et al. 2005] Parkin DM, et al. "Global cancer statistics, 2002." CA: a cancer journal for clinicians 55.2 (2005): 74-108.
[Song et al. 2013] Song Q, et al. "Optimal co-segmentation of tumor in PET-CT images with context information." IEEE Transactions on Medical Imaging 32.9, 1685-1697 (2013).
[Vallières et al. 2017] Vallières M, et al. “Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer.” Nature Scientific Reports, 7(1):10117 (2017).