论文标题
头颈肿瘤分割的多尺度融合方法
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation
论文作者
论文摘要
头部和颈部（H \＆N）有机危（OAR）和肿瘤分割是放射治疗计划的重要组成部分。由于缺乏准确且可靠的划定方法，很难获得H \＆n Nodal总肿瘤体积（GTVN）和H \＆N原发性肿瘤体积（GTVP）的不同解剖位置和尺寸。不正确分割的下游效应可能导致不必要的正常器官辐照。为了采用全自动放射疗法计划算法，我们探讨了基于多尺度融合的深度学习体系结构的功效，以准确从医疗扫描中细分H \＆n肿瘤。
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.
