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PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT

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Auto-segmentation of head and neck cancer (HNC) primary gross tumor volume (GTVt) is a necessary but challenging process for radiotherapy treatment planning and radiomics studies. The HEad and neCK TumOR Segmentation Challenge (HECKTOR) 2021 comprises two major tasks: auto-segmentation of GTVt in FDG-PET/CT images and the prediction of patient outcomes. In this paper, we focus on the segmentation part by proposing two PET normalization methods to mitigate impacts from intensity variances between PET scans for deep learning-based GTVt auto-segmentation. We also compared the performance of three popular hybrid loss functions. An ensemble of our proposed models achieved an average Dice Similarity Coefficient (DSC) of 0.779 and median 95% Hausdorff Distance (HD95) of 3.15 mm on the test set. Team: Aarhus_Oslo.
Original languageEnglish
Title of host publicationHead and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings : Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
Number of pages9
Place of publicationCham
Publication yearMar 2022
ISBN (print) 978-3-030-98252-2
ISBN (Electronic)978-3-030-98253-9
Publication statusPublished - Mar 2022
SeriesLecture Notes in Computer Science

    Research areas

  • Auto-segmentation, Deep learning, Gross tumor volume, Head and neck cancer

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