Thyroid carcinoma is one of the most common endocrine diseases globally, and the incidence has been on the rise in recent years. Ultrasound imaging is the primary clinical method for early thyroid nodule diagnosis. Regions of interest (ROIs) of nodules in ultrasound images are difficult to detect because of their irregular shape nand vague margins. Accurate real-time thyroid nodule detection can provide ROIs for subsequent nodule diagnosis automatically, avoid variabilities between the subjective interpretations and inter-observer effectively and alleviate the workloads of medical practitioners. The aim of this study was to present a reliable, real-time detection method based on the Faster R-CNN (region-based convolutional network) framework for accurate and fast detection of thyroid nodules in ultrasound images. Our study proposed a faster and more accurate thyroid nodule detection method based on the Faster R-CNN framework by adding three strategies: feature pyramid, spatial remapping and anchor-box redesign. Specifically, the network takes raw ultrasound images as inputs and generates boxes with positions and the possibilities that these boxes contain thyroid nodules. The proposed method could locate and detect target nodules accurately with a mean average precision of 92.79% with more than 9000 patient images. In addition, the detection rate has accelerated to >16 frames per second, four times faster than that of the initial network. Therefore, it can meet the requirements of clinical application. The performance of the fivefold cross-validation was also accurate and robust. The proposed automatic thyroid nodule detection method yields better performance in accuracy and detection speed, which indicates the potential value of our method in assisting clinical ultrasound image interpretation.

甲状腺癌是全球最常见的内分泌疾病之一，近年来发病率呈上升趋势。超声成像是早期甲状腺结节诊断的主要临床方法。超声图像中结节的感兴趣区域 (ROI) 由于形状不规则和边缘模糊而难以检测。准确的实时甲状腺结节检测可以自动为后续的结节诊断提供投资回报率，有效避免主观解释和观察者之间的差异，减轻医生的工作量。本研究的目的是提出一种基于 Faster R-CNN（基于区域的卷积网络）框架的可靠、实时检测方法，用于准确快速地检测超声图像中的甲状腺结节。我们的研究提出了一种基于 Faster R-CNN 框架的更快、更准确的甲状腺结节检测方法，通过添加三种策略：特征金字塔、空间重映射和锚框重新设计。具体来说，网络将原始超声图像作为输入并生成带有位置的框以及这些框包含甲状腺结节的可能性。所提出的方法可以准确定位和检测目标结节，平均精度为 92.79%，超过 9000 幅患者图像。此外，检测速度已加速到每秒 16 帧以上，比初始网络快四倍。因此，它可以满足临床应用的要求。五重交叉验证的性能也是准确和稳健的。