In the radiation therapy of nasopharyngeal carcinoma (NPC), clinicians typically delineate the gross tumor volume (GTV) using non-contrast planning computed tomography to ensure accurate radiation dose delivery. However, the low contrast between tumors and adjacent normal tissues necessitates that radiation oncologists manually delineate the tumors, often relying on diagnostic MRI for guidance. % In this study, we propose a novel approach to directly segment NPC gross tumors on non-contrast planning CT images, circumventing potential registration errors when aligning MRI or MRI-derived tumor masks to planning CT. To address the low contrast issues between tumors and adjacent normal structures in planning CT, we introduce a 3D Semantic Asymmetry Tumor segmentation (SATs) method. Specifically, we posit that a healthy nasopharyngeal region is characteristically bilaterally symmetric, whereas the emergence of nasopharyngeal carcinoma disrupts this symmetry. Then, we propose a Siamese contrastive learning segmentation framework that minimizes the voxel-wise distance between original and flipped areas without tumor and encourages a larger distance between original and flipped areas with tumor. Thus, our approach enhances the sensitivity of features to semantic asymmetries. % Extensive experiments demonstrate that the proposed SATs achieves the leading NPC GTV segmentation performance in both internal and external testing, \emph{e.g.}, with at least 2\% absolute Dice score improvement and 12\% average distance error reduction when compared to other state-of-the-art methods in the external testing.

翻译：在鼻咽癌（NPC）的放射治疗中，临床医生通常使用非增强计划计算机断层扫描（CT）来勾画大体肿瘤体积（GTV），以确保放射剂量的精准投递。然而，肿瘤与邻近正常组织之间的低对比度要求放射肿瘤科医生手动勾画肿瘤，且常常依赖诊断性磁共振成像（MRI）作为指导。本研究提出了一种新方法，旨在非增强计划CT图像上直接分割鼻咽癌大体肿瘤，从而规避将MRI或MRI衍生的肿瘤掩模与计划CT对齐时可能产生的配准误差。为解决计划CT中肿瘤与邻近正常结构间的低对比度问题，我们引入了一种三维语义不对称肿瘤分割（SATs）方法。具体而言，我们假设健康的鼻咽区域具有典型的双侧对称性，而鼻咽癌的出现破坏了这种对称性。基于此，我们提出了一种孪生对比学习分割框架，该框架最小化无肿瘤区域的原图与翻转图像素间的距离，同时鼓励有肿瘤区域的原图与翻转图像素间产生更大的距离。因此，我们的方法增强了特征对语义不对称性的敏感性。大量实验表明，所提出的SATs方法在内部和外部测试中均取得了领先的鼻咽癌GTV分割性能，例如，在外部测试中，与其他最先进方法相比，其绝对Dice分数至少提高了2%，平均距离误差降低了12%。