Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation
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Abstract (arXiv)
Head and Neck (H&N) organ-at-risk (OAR) and tumor segmentations are essential components 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 29 Oct 2022.
Builds on
9 of 13 references.
References · 9
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,417
- 2021Transformers in Vision: A Surveycited by 1,009
- 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
- 2021MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentationcited by 53
- 2022Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Imagescited by 29
- 2021GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentationcited by 11
- 2022An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentationcited by 4
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Citing papers per year: 2023: 1.
1 paper.