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Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation

Authors : Abhishek Srivastava, Debesh Jha, Bulent Aydogan, Mohamed E. Abazeed, Ulas Bagci

Published in: Head and Neck Tumor Segmentation and Outcome Prediction

Publisher: Springer Nature Switzerland

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Abstract

Head and Neck (H &N) organ-at-risk (OAR) and tumor segmentations are an essential component of radiation therapy planning. The varying anatomic locations and dimensions of H &N nodal Gross Tumor Volumes (GTVn) and H &N primary gross tumor volume (GTVp) are difficult to obtain due to lack of accurate and reliable delineation methods. The downstream effect of incorrect segmentation can result in unnecessary irradiation of normal organs. Towards a fully automated radiation therapy planning algorithm, we explore the efficacy of multi-scale fusion based deep learning architectures for accurately segmenting H &N tumors from medical scans. Team Name: M &H_lab_NU.

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previous chapter A U-Net Convolutional Neural Network with Multiclass Dice Loss for Automated Segmentation of Tumors and Lymph Nodes from Head and Neck Cancer PET/CT Images
next chapter Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach
Literature
1.
Aerts, H.J., et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5(1), 1–9 (2014)MathSciNet
2.
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.) HECKTOR 2021. LNCS, vol. 13209, pp. 1–37. Springer, Cham (2021). https://​doi.​org/​10.​1007/​978-3-030-98253-9_​1CrossRef
3.
Dosovitskiy, A., et al.: An image is worth 16\(\times 16\) words: transformers for image recognition at scale. arXiv preprint arXiv:​2010.​11929 (2020)
4.
5.
6.
Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: a survey. ACM Comput. Surv. (CSUR) 54(10s), 1–41 (2021)CrossRef
7.
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
8.
Lou, B., et al.: An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction. Lancet Digit. Health 1(3), e136–e147 (2019)CrossRef
9.
Oreiller, V., et al.: Head and neck tumor segmentation in PET/CT: the HECKTOR challenge. Med. Image Anal. 77, 102336 (2022)
10.
11.
Srivastava, A., Chanda, S., Jha, D., Pal, U., Ali, S.: GMSRF-Net: an improved generalizability with global multi-scale residual fusion network for polyp segmentation. arXiv preprint arXiv:​2111.​10614 (2021)
12.
Srivastava, A., et al.: MSRF-Net: a multi-scale residual fusion network for biomedical image segmentation. IEEE J. Biomed. Health Inform. 26(5), 2252–2263 (2021)MathSciNetCrossRef
13.
Srivastava, A., Jha, D., Keles, E., Aydogan, B., Abazeed, M., Bagci, U.: An efficient multi-scale fusion network for 3D organ at risk (OAR) segmentation. arXiv preprint arXiv:​2208.​07417 (2022)
14.
Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. 43(10), 3349–3364 (2020)CrossRef
Metadata
Title
Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation
Authors
Abhishek Srivastava
Debesh Jha
Bulent Aydogan
Mohamed E. Abazeed
Ulas Bagci
Copyright Year
2023
Book
Head and Neck Tumor Segmentation and Outcome Prediction

Print ISBN: 978-3-031-27419-0

Electronic ISBN: 978-3-031-27420-6

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-27420-6_11

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