Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation
This addresses the need for reliable tumor delineation in radiation therapy to avoid unnecessary irradiation of normal organs, but it appears incremental as it builds on existing deep learning methods.
The paper tackled the problem of accurately segmenting head and neck tumors from medical scans for radiation therapy planning, and the result was an exploration of multi-scale fusion deep learning architectures to improve segmentation accuracy, though no concrete numbers are provided.
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.