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Automated Head and Neck Tumor Segmentation from 3D PET/CT HECKTOR 2022 Challenge Report

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Head and Neck Tumor Segmentation and Outcome Prediction (HECKTOR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13626))

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Abstract

Head and neck tumor segmentation challenge (HECKTOR) 2022 offers a platform for researchers to compare their solutions to segmentation of tumors and lymph nodes from 3D CT and PET images. In this work, we describe our solution to HECKTOR 2022 segmentation task. We re-sample all images to a common resolution, crop around head and neck region, and train SegResNet semantic segmentation network from MONAI. We use 5-fold cross validation to select best model checkpoint. The final submission is an ensemble of 15 models from 3 runs. Our solution (team name NVAUTO) achieves the 1st place on the HECKTOR22 challenge leaderboard with an aggregated dice score of 0.78802 (https://hecktor.grand-challenge.org/evaluation/segmentation/leaderboard/.). It is implemented with Auto3DSeg (https://monai.io/apps/auto3dseg.).

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Notes

  1. 1.

    https://github.com/Project-MONAI/MONAI.

  2. 2.

    https://monai.io/apps/auto3dseg.

  3. 3.

    https://docs.monai.io/en/stable/networks.html.

  4. 4.

    https://hecktor.grand-challenge.org/evaluation/segmentation/leaderboard/.

References

  1. Project-monai/monai. https://doi.org/10.5281/zenodo.5083813

  2. Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2022: automatic head and neck tumor segmentation and outcome prediction in PET/CT (2023). https://arxiv.org/abs/2201.04138

  3. He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, Sun, Jian: Identity mappings in deep residual networks. In: Leibe, Bastian, Matas, Jiri, Sebe, Nicu, Welling, Max (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630ā€“645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38

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  4. Myronenko, Andriy: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, Alessandro, Bakas, Spyridon, Kuijf, Hugo, Keyvan, Farahani, Reyes, Mauricio, van Walsum, Theo (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311ā€“320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28

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  5. Oreiller, V., et al.: Head and neck tumor segmentation in PET/CT: the HECKTOR challenge. Med. Image Anal. 77, 102336 (2022)

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Correspondence to Andriy Myronenko .

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Myronenko, A., Siddiquee, M.M.R., Yang, D., He, Y., Xu, D. (2023). Automated Head and Neck Tumor Segmentation from 3D PET/CT HECKTOR 2022 Challenge Report. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2022. Lecture Notes in Computer Science, vol 13626. Springer, Cham. https://doi.org/10.1007/978-3-031-27420-6_2

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  • DOI: https://doi.org/10.1007/978-3-031-27420-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27419-0

  • Online ISBN: 978-3-031-27420-6

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