mlsanityData to 7 Oct 2026

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

arXiv 2201.04138 ↗First posted 11 Jan 2022eess.IV cs.CVcited by 29

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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.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 11 of its 72 references that are in the map. First posted on arXiv 11 Jan 2022.

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11 of 72 references.

References · 11

  1. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
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  7. 2017Radiomics strategies for risk assessment of tumour failure in head-and-neck cancercited by 31
  8. 2021Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Imagescited by 20
  9. 2018Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancercited by 14
  10. 2021Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attentioncited by 6
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 7; 2024: 11; 2025: 3; 2026: 1.

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  1. 2024A review of deep learning-based information fusion techniques for multimodal medical image classificationcited by 25
  2. 2022An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Datacited by 13
  3. 2022Automated head and neck tumor segmentation from 3D PET/CTcited by 9
  4. 2023Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancercited by 9
  5. 2023SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Imagescited by 5
  6. 2022ISA-Net: Improved spatial attention network for PET-CT tumor segmentationcited by 3
  7. 2023AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Imagescited by 3
  8. 2024Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimescited by 3
  9. 2022Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networkscited by 2
  10. 2023Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON planscited by 2
  11. 2024PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentationcited by 2
  12. 2024On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Modelscited by 2
17 more
  1. 2022Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentationcited by 1
  2. 2022Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT imagescited by 1
  3. 2024HuLP: Human-in-the-Loop for Prognosiscited by 1
  4. 2024S&D Messenger: Exchanging Semantic and Domain Knowledge for Generic Semi-Supervised Medical Image Segmentationcited by 1
  5. 2024Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancercited by 1
  6. 2022Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT imagescited by 0
  7. 2023Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patientscited by 0
  8. 2023Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligencecited by 0
  9. 2023Academic competitionscited by 0
  10. 2024Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentationcited by 0
  11. 2024Deep Learning-Based Segmentation of Tumors in PET/CT Volumes: Benchmark of Different Architectures and Training Strategiescited by 0
  12. 2024IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Studycited by 0
  13. 2024SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Networkcited by 0
  14. 20253D Medical Imaging Segmentation on Non-Contrast CTcited by 0
  15. 2025Benchmarking Deep Learning Models for Laryngeal Cancer Staging Using the LaryngealCT Datasetcited by 0
  16. 2025MAFM^3: Modular Adaptation of Foundation Models for Multi-Modal Medical AIcited by 0
  17. 2026A Specialized Large Multimodal Model for Interpreting PET/CT in Head and Neck Cancercited by 0