BACKGROUND:Thyroid ultrasonography is widely used to diagnose thyroid nodules in clinics. Automatic localization of nodules can promote the development of intelligent thyroid diagnosis and reduce workload of radiologists. However, besides the ultrasound image has low contrast and high noise, the thyroid nodules are diverse in shape and vary greatly in size. Thus, thyroid nodule detection in ultrasound images is still a challenging task. OBJECTIVE:This study proposes an automatic detection algorithm to locate nodules in B ultrasound images and Doppler ultrasound images. This method can be used to screen thyroid nodules and provide a basis for subsequent automatic segmentation and intelligent diagnosis. METHODS:We develop and optimize an improved YOLOV3 model for detecting thyroid nodules in ultrasound images with B-mode and Doppler mode. Improvements include (1) using the high-resolution network (HRNet) as the basic network for gradually extracting high-level semantic features to reduce the missed detection and misdetection, (2) optimizing the loss function for single target detection like nodules, and (3) obtaining the anchor boxes by clustering the candidate frames of real nodules in the dataset. RESULTS:The experimental results of applying to 8000 clinical ultrasound images show that the new method developed and tested in this study can effectively detect thyroid nodules. The method achieves 94.53% mean precision and 95.00% mean recall. CONCLUTIONS:The study demonstrates a new automated method that enables to achieve high detection accuracy and effectively locate thyroid nodules in various ultrasound images without any user interaction, which indicates its potential clinical application value for the thyroid nodule screening.

中文翻译：

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使用改进的 YOLOV3 网络自动定位超声图像中的甲状腺结节

背景：甲状腺超声在临床上广泛用于诊断甲状腺结节。结节的自动定位可以促进甲状腺智能诊断的发展，减轻放射科医师的工作量。然而，除了超声图像对比度低、噪声大外，甲状腺结节形状多样，大小差异很大。因此，超声图像中的甲状腺结节检测仍然是一项具有挑战性的任务。目的：本研究提出了一种自动检测算法来定位 B 超声图像和多普勒超声图像中的结节。该方法可用于甲状腺结节的筛查，为后续的自动分割和智能诊断提供依据。方法：我们开发并优化了改进的 YOLOV3 模型，用于在 B 模式和多普勒模式下检测超声图像中的甲状腺结节。改进包括（1）使用高分辨率网络（HRNet）作为基础网络，逐步提取高级语义特征以减少漏检和误检，（2）优化针对结节等单目标检测的损失函数，以及（ 3）通过聚类数据集中真实结节的候选框获得锚框。结果：应用于8000张临床超声图像的实验结果表明，本研究开发和测试的新方法可以有效地检测甲状腺结节。该方法实现了 94.53% 的平均精度和 95.00% 的平均召回率。结论：该研究展示了一种新的自动化方法，无需任何用户交互即可实现高检测精度并有效定位各种超声图像中的甲状腺结节，