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1
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
2
Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
3
The University of Texas MD Anderson Cancer Center, Houston, USA.
4
Geneva University Hospital, Geneva, Switzerland.
5
University Hospital Zürich, Zurich, Switzerland.
6
Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France.
7
Radiotherapy Department, Cancer Institute Eugène Marquis, Rennes, France.
8
INSERM, U1099, Rennes, France.
9
University of Rennes 1, LTSI, Rennes, France.
10
Centre Hospitalier Universitaire de Poitiers (CHUP), Poitiers, France.
11
LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.
12
Cleveland Clinic Foundation, Department of Radiation Oncology, Cleveland, OH, USA.
13
Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada.
1
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
2
Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland.
3
The University of Texas MD Anderson Cancer Center, Houston, USA.
4
Geneva University Hospital, Geneva, Switzerland.
5
University Hospital Zürich, Zurich, Switzerland.
6
Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France.
7
Radiotherapy Department, Cancer Institute Eugène Marquis, Rennes, France.
8
INSERM, U1099, Rennes, France.
9
University of Rennes 1, LTSI, Rennes, France.
10
Centre Hospitalier Universitaire de Poitiers (CHUP), Poitiers, France.
11
LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.
12
Cleveland Clinic Foundation, Department of Radiation Oncology, Cleveland, OH, USA.
13
Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada.
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 (
DSC
agg
) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 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.
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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
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
