English

Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation

Image and Video Processing 2022-11-01 v1 Computer Vision and Pattern Recognition
Authors: Abhishek Srivastava , Debesh Jha , Bulent Aydogan , Mohamed E. Abazeed , Ulas Bagci
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Abstract

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.

Keywords

brain tumor segmentation cancer detection and segmentation medical image segmentation

Cite

@article{arxiv.2210.16704,
  title  = {Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation},
  author = {Abhishek Srivastava and Debesh Jha and Bulent Aydogan and Mohamed E. Abazeed and Ulas Bagci},
  journal= {arXiv preprint arXiv:2210.16704},
  year   = {2022}
}

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R2 v1 2026-06-28T04:46:47.817Z