# Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images arXiv: https://arxiv.org/abs/2201.04138 ยท HTML: https://mlsanity.com/paper/2201.04138/ ## Key facts - Cited by 30 papers in the map (data through 14 Oct 2026). - Builds on 11 of its 72 references that are in the map. - First posted on arXiv 11 Jan 2022. ## Builds on (most cited first, 11 of 11) - [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016) - [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018) - [Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge](https://mlsanity.com/paper/1904.00682/) (2019) - [Why rankings of biomedical image analysis competitions should be interpreted with care](https://mlsanity.com/paper/1806.02051/) (2018) - [BIAS: Transparent reporting of biomedical image analysis challenges](https://mlsanity.com/paper/1910.04071/) (2019) - [Mumford-Shah Loss Functional for Image Segmentation with Deep Learning](https://mlsanity.com/paper/1904.02872/) (2019) - [Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer](https://mlsanity.com/paper/1703.08516/) (2017) - [Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images](https://mlsanity.com/paper/2102.10446/) (2021) - [Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer](https://mlsanity.com/paper/1810.02492/) (2018) - [Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention](https://mlsanity.com/paper/2111.03848/) (2021) - [Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers](https://mlsanity.com/paper/1908.00841/) (2019) ## Built on by (most cited first, 30 of 30) - [A review of deep learning-based information fusion techniques for multimodal medical image classification](https://mlsanity.com/paper/2404.15022/) (2024) - [An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data](https://mlsanity.com/paper/2202.12537/) (2022) - [Automated head and neck tumor segmentation from 3D PET/CT](https://mlsanity.com/paper/2209.10809/) (2022) - [Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer](https://mlsanity.com/paper/2307.03427/) (2023) - [SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images](https://mlsanity.com/paper/2302.03861/) (2023) - [ISA-Net: Improved spatial attention network for PET-CT tumor segmentation](https://mlsanity.com/paper/2211.02256/) (2022) - [AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images](https://mlsanity.com/paper/2305.09946/) (2023) - [Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes](https://mlsanity.com/paper/2406.11659/) (2024) - [Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks](https://mlsanity.com/paper/2202.07823/) (2022) - [Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans](https://mlsanity.com/paper/2309.13747/) (2023) - [PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2404.13704/) (2024) - [On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models](https://mlsanity.com/paper/2406.08486/) (2024) - [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026) - [Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation](https://mlsanity.com/paper/2210.16704/) (2022) - [Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images](https://mlsanity.com/paper/2211.10138/) (2022) - [HuLP: Human-in-the-Loop for Prognosis](https://mlsanity.com/paper/2403.13078/) (2024) - [S&D Messenger: Exchanging Semantic and Domain Knowledge for Generic Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.07763/) (2024) - [Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer](https://mlsanity.com/paper/2409.06605/) (2024) - [Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images](https://mlsanity.com/paper/2207.01623/) (2022) - [Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients](https://mlsanity.com/paper/2304.08106/) (2023) - [Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence](https://mlsanity.com/paper/2306.00034/) (2023) - [Academic competitions](https://mlsanity.com/paper/2312.00268/) (2023) - [Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation](https://mlsanity.com/paper/2401.06180/) (2024) - [Deep Learning-Based Segmentation of Tumors in PET/CT Volumes: Benchmark of Different Architectures and Training Strategies](https://mlsanity.com/paper/2404.09761/) (2024) - [IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study](https://mlsanity.com/paper/2405.00239/) (2024) - [SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network](https://mlsanity.com/paper/2409.19901/) (2024) - [3D Medical Imaging Segmentation on Non-Contrast CT](https://mlsanity.com/paper/2503.08361/) (2025) - [Benchmarking Deep Learning Models for Laryngeal Cancer Staging Using the LaryngealCT Dataset](https://mlsanity.com/paper/2510.11047/) (2025) - [MAFM^3: Modular Adaptation of Foundation Models for Multi-Modal Medical AI](https://mlsanity.com/paper/2511.11212/) (2025) - [A Specialized Large Multimodal Model for Interpreting PET/CT in Head and Neck Cancer](https://mlsanity.com/paper/2609.05532/) (2026) ## Abstract (arXiv) This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. The challenge is composed of three tasks related to the automatic analysis of PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the automatic segmentation of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the automatic prediction of Progression Free Survival (PFS) from the same FDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVt annotations provided to the participants. The data were collected from six centers for a total of 325 images, split into 224 training and 101 testing cases. The interest in the challenge was highlighted by the important participation with 103 registered teams and 448 result submissions. The best methods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the first task, and a Concordance index (C-index) of 0.7196 and 0.6978 in Tasks 2 and 3, respectively. In all tasks, simplicity of the approach was found to be key to ensure generalization performance. The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used. This potentially obviates the need for GTVt contouring, opening avenues for reproducible and large scale radiomics studies including thousands potential subjects.