Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023:13626:1-30.
doi: 10.1007/978-3-031-27420-6_1. Epub 2023 Mar 18.

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

Vincent Andrearczyk  1 , Valentin Oreiller  1   2 , Moamen Abobakr  3 , Azadeh Akhavanallaf  4 , Panagiotis Balermpas  5 , Sarah Boughdad  2 , Leo Capriotti  6 , Joel Castelli  7   8   9 , Catherine Cheze Le Rest  10   11 , Pierre Decazes  6 , Ricardo Correia  2 , Dina El-Habashy  3 , Hesham Elhalawani  12 , Clifton D Fuller  3 , Mario Jreige  2 , Yornna Khamis  3 , Agustina La Greca  5 , Abdallah Mohamed  3 , Mohamed Naser  3 , John O Prior  2 , Su Ruan  6 , Stephanie Tanadini-Lang  5 , Olena Tankyevych  10   11 , Yazdan Salimi  4 , Martin Vallières  13 , Pierre Vera  6 , Dimitris Visvikis  11 , Kareem Wahid  3 , Habib Zaidi  4 , Mathieu Hatt  11 , Adrien Depeursinge  1   2
Affiliations

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

Vincent Andrearczyk et al. Head Neck Tumor Chall (2022). 2023.

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

Keywords: Challenge; Deep learning; Head and neck cancer; Machine learning; Medical imaging; Radiomics; Segmentation.

PubMed Disclaimer

Conflict of interest statement

No conflict of interest applies. Fundings are specified in the acknowledgments. Only the organizers had access to the test cases’ ground truth contours.

Figures

Fig. 1.
Fig. 1.
Case examples of 2D sagittal slices of fused PET/CT images from each of the nine centers, showing the variety of fields of view. The CT (grayscale) window in Hounsfield unit is [−140, 260] and the PET window in SUV is [0, 12], represented in a “hot” colormap.
Fig. 2.
Fig. 2.
Examples of results of the winning team (NVAUTO [32]). The automatic segmentation results (light) and ground truth annotations (dark) are displayed on an overlay of 2D slices of CT (left) images and PET (right). GTVn is in red and GTVp in blue. CT are clipped between [−140,260] HU and PET images are between [0,5] SUV.
See this image and copyright information in PMC

References

    1. Ahamed S, Polson L, Rahmim A: A U-Net convolutional neural network with multiclass Dice loss for automated segmentation of tumors and lymph nodes from head and neck cancer PET/CT images. In: Lecture Notes in Computer Science (LNCS) Challenges (2023)
    1. Andrearczyk V, et al. : Automatic head and neck tumor segmentation and outcome prediction relying on FDG-PET/CT images: findings from the second edition of the HECKTOR challenge. Medical Image Analysis (in review) - PubMed
    1. 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: Andrearczyk V, Oreiller V, Hatt M, Depeursinge A (eds.) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2021. LNCS, vol. 13209, pp. 1–37. Springer, Cham: (2022). 10.1007/978-3-030-98253-9.1 - DOI - PMC - PubMed
    1. Andrearczyk V, Oreiller V, Depeursinge A: Oropharynx detection in PET-CT for tumor segmentation. Irish Mach. Vis. Image Process, 109–112 (2020)
    1. Andrearczyk V, Oreiller V, Jreige M, Castelli J, Prior JO, Depeursinge A: Segmentation and classification of head and neck nodal metastases and primary tumors in PET/CT. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4731–4735. IEEE; (2022) - PubMed

LinkOut - more resources