@inproceedings{e5af1a6eaab340b89f6835df9c0eacd4,
title = "PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT",
abstract = "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.",
keywords = "Auto-segmentation, Deep learning, Gross tumor volume, Head and neck cancer",
author = "Jintao Ren and Huynh, {Bao Ngoc} and Groendahl, {Aurora Rosvoll} and Oliver Tomic and Futsaether, {Cecilia Marie} and Korreman, {Stine Sofia}",
year = "2022",
month = mar,
doi = "10.1007/978-3-030-98253-9_7",
language = "English",
isbn = " 978-3-030-98252-2",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "83--91",
editor = "Vincent Andrearczyk and Valentin Oreiller and Mathieu Hatt and Adrien Depeursinge",
booktitle = "Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings",
address = "Netherlands",
note = "2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 27-09-2021",
}