Thyroid nodule segmentation is a crucial step in the diagnostic procedure of physicians and computer-aided diagnosis systems. Mostly, current studies treat segmentation and diagnosis as independent tasks without considering the correlation between these tasks. The sequence steps of these independent tasks in computer-aided diagnosis systems may lead to the accumulation of errors. Therefore, it is worth combining them as a whole through exploring the relationship between thyroid nodule segmentation and diagnosis. According to the thyroid imaging reporting and data system (TI-RADS), the assessment of shape and margin characteristics is the prerequisite for the discrimination of benign and malignant thyroid nodules. These characteristics can be observed in the thyroid nodule segmentation masks. Inspired by the diagnostic procedure of TI-RADS, this paper proposes a shape-margin knowledge augmented network (SkaNet) for simultaneously thyroid nodule segmentation and diagnosis. Due to the similarity in visual features between segmentation and diagnosis, SkaNet shares visual features in the feature extraction stage and then utilizes a dual-branch architecture to perform thyroid nodule segmentation and diagnosis tasks simultaneously. To enhance effective discriminative features, an exponential mixture module is devised, which incorporates convolutional feature maps and self-attention maps by exponential weighting. Then, SkaNet is jointly optimized by a knowledge augmented multi-task loss function with a constraint penalty term. It embeds shape and margin characteristics through numerical computation and models the relationship between the thyroid nodule diagnosis results and segmentation masks.

翻译：甲状腺结节分割是医生诊断流程及计算机辅助诊断系统中的关键步骤。当前，多数研究将分割与诊断视为独立任务，未考虑两者间的相关性。在计算机辅助诊断系统中，这些独立任务的顺序执行可能导致误差累积。因此，通过探索甲状腺结节分割与诊断之间的关系，将其整合为一个整体具有重要意义。根据甲状腺影像报告和数据系统（TI-RADS），形状与边缘特征评估是鉴别甲状腺结节良恶性的先决条件。这些特征可在甲状腺结节分割掩膜中观察到。受TI-RADS诊断流程启发，本文提出一种形状-边缘知识增强网络（SkaNet），用于同步实现甲状腺结节分割与诊断。由于分割与诊断在视觉特征上的相似性，SkaNet在特征提取阶段共享视觉特征，随后利用双分支架构同时执行甲状腺结节分割与诊断任务。为增强有效判别特征，设计了指数混合模块，通过指数加权融合卷积特征图与自注意力图。进一步，采用带有约束惩罚项的知识增强多任务损失函数联合优化SkaNet，通过数值计算嵌入形状与边缘特征，并建模甲状腺结节诊断结果与分割掩膜之间的关系。