Segmentation of nodules in thyroid ultrasound imaging plays a crucial role in the detection and treatment of thyroid cancer. However, owing to the diversity of scanner vendors and imaging protocols in different hospitals, the automatic segmentation model, which has already demonstrated expert-level accuracy in the field of medical image segmentation, finds its accuracy reduced as the result of its weak generalization performance when being applied in clinically realistic environments. To address this issue, the present paper proposes ASTN, a framework for thyroid nodule segmentation achieved through a new type co-registration network. By extracting latent semantic information from the atlas and target images and utilizing in-depth features to accomplish the co-registration of nodules in thyroid ultrasound images, this framework can ensure the integrity of anatomical structure and reduce the impact on segmentation as the result of overall differences in image caused by different devices. In addition, this paper also provides an atlas selection algorithm to mitigate the difficulty of co-registration. As shown by the evaluation results collected from the datasets of different devices, thanks to the method we proposed, the model generalization has been greatly improved while maintaining a high level of segmentation accuracy.

翻译：甲状腺结节超声图像分割在甲状腺癌的检测与治疗中起着关键作用。然而，由于不同医院中超声设备供应商和成像协议的多样性，已在医学图像分割领域展现出专家级准确率的自动分割模型，在临床真实环境中的应用效果因其泛化性能不足而准确性下降。针对这一问题，本文提出ASTN——一种通过新型协同配准网络实现的甲状腺结节分割框架。该框架通过从图谱图像和目标图像中提取潜在语义信息，并利用深度特征完成甲状腺超声图像中结节的协同配准，既能保证解剖结构的完整性，又能减少因不同设备导致的整体图像差异对分割效果的影响。此外，本文还提供了一种图谱选择算法以缓解协同配准的难度。来自不同设备数据集的评估结果表明，所提出的方法在保持较高分割准确率的同时，显著提升了模型的泛化能力。