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2023 | OriginalPaper | Chapter

Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach

Authors : Hung Chu, Luis Ricardo De la O Arévalo, Wei Tang, Baoqiang Ma, Yan Li, Alessia De Biase, Stefan Both, Johannes Albertus Langendijk, Peter van Ooijen, Nanna Maria Sijtsema, Lisanne V. van Dijk

Published in: Head and Neck Tumor Segmentation and Outcome Prediction

Publisher: Springer Nature Switzerland

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Abstract

Delineation of Gross Tumor Volume (GTV) is essential for the treatment of cancer with radiotherapy. GTV contouring is a time-consuming specialized manual task performed by radiation oncologists. Deep Learning (DL) algorithms have shown potential in creating automatic segmentations, reducing delineation time and inter-observer variation. The aim of this work was to create automatic segmentations of primary tumors (GTVp) and pathological lymph nodes (GTVn) in oropharyngeal cancer patients using DL. The organizers of the HECKTOR 2022 challenge provided 3D Computed Tomography (CT) and Positron Emission Tomography (PET) scans with ground-truth GTV segmentations acquired from nine different centers. Bounding box cropping was applied to obtain an anatomic based region of interest. We used the Swin UNETR model in combination with transfer learning. The Swin UNETR encoder weights were initialized by pre-trained weights of a self-supervised Swin UNETR model. An average Dice score of 0.656 was achieved on a test set of 359 patients from the HECKTOR 2022 challenge. Code is available at: https://​github.​com/​HC94/​swin_​unetr_​hecktor_​2022.
Aicrowd Group Name: RT_UMCG

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Literature
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Metadata
Title
Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach
Authors
Hung Chu
Luis Ricardo De la O Arévalo
Wei Tang
Baoqiang Ma
Yan Li
Alessia De Biase
Stefan Both
Johannes Albertus Langendijk
Peter van Ooijen
Nanna Maria Sijtsema
Lisanne V. van Dijk
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-27420-6_12

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