@inproceedings{f44f7fe729134fe2879100ac8628d355,
title = "Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images",
abstract = "One of the primary treatment options for head and neck cancer is (chemo)radiation. Accurate delineation of the contour of the tumors is of great importance in the successful treatment of the tumor and in the prediction of patient outcomes. With this paper we take part in the HECKTOR 2021 challenge and we propose our methods for automatic tumor segmentation on PET and CT images of oropharyngeal cancer patients. To achieve this goal, we investigated different deep learning methods with the purpose of highlighting relevant image and modality related features, to refine the contour of the primary tumor. More specifically, we tested a Co-learning method [1] and a 3D Skip Spatial and Channel Squeeze and Excitation Multi-Scale Attention method (Skip-scSE-M), on the challenge dataset. The best results achieved on the test set were 0.762 mean Dice Similarity Score and 3.143 median of the Hausdorf Distance at 95 \%.",
keywords = "AI, Automatic segmentation, Co-learning, Deep learning, Head and neck cancer, Head and neck tumor segmentation, HECKTOR2021, Multi-modal, Neural networks, Normalization, Oropharynx, PET-CT, Radiotherapy treatment planning, Squeeze-and-excitation",
author = "\{De Biase\}, Alessia and Wei Tang and Nikos Sourlos and Baoqiang Ma and Jiapan Guo and Sijtsema, \{Nanna Maria\} and \{van Ooijen\}, Peter",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 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",
year = "2022",
doi = "10.1007/978-3-030-98253-9\_10",
language = "English",
isbn = "9783030982522",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "109--120",
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 = "Singapore",
}