多普勒增强深度学习：基于YOLOv5实例分割的甲状腺结节分割优化

摘要

全球甲状腺癌发病率的不断上升推动了各种计算机辅助检测方法的发展。甲状腺结节的精确分割是开发AI辅助临床决策支持系统的关键第一步。本研究基于超声图像采用YOLOv5算法实现甲状腺结节实例分割。我们在包含多普勒图像与不包含多普勒图像的两种数据集版本上，评估了五种YOLOv5变体（Nano/Small/Medium/Large/XLarge）。YOLOv5-Large算法在包含多普勒图像的数据集上表现最佳，其Dice相似系数达91%，平均精度均值为0.87。值得注意的是，通常被医师排除的多普勒图像能显著提升分割性能：排除多普勒图像时YOLOv5-Small模型的Dice系数为79%，而引入多普勒图像后所有模型变体性能均得到提升。这些发现表明，基于YOLOv5的实例分割技术为甲状腺结节检测提供了有效的实时解决方案，在自动化诊断系统中具有临床应用潜力。

English

The increasing prevalence of thyroid cancer globally has led to the development of various computer-aided detection methods. Accurate segmentation of thyroid nodules is a critical first step in the development of AI-assisted clinical decision support systems. This study focuses on instance segmentation of thyroid nodules using YOLOv5 algorithms on ultrasound images. We evaluated multiple YOLOv5 variants (Nano, Small, Medium, Large, and XLarge) across two dataset versions, with and without doppler images. The YOLOv5-Large algorithm achieved the highest performance with a dice score of 91\% and mAP of 0.87 on the dataset including doppler images. Notably, our results demonstrate that doppler images, typically excluded by physicians, can significantly improve segmentation performance. The YOLOv5-Small model achieved 79\% dice score when doppler images were excluded, while including them improved performance across all model variants. These findings suggest that instance segmentation with YOLOv5 provides an effective real-time approach for thyroid nodule detection, with potential clinical applications in automated diagnostic systems.

多普勒增强深度学习：基于YOLOv5实例分割的甲状腺结节分割优化

Doppler-Enhanced Deep Learning: Improving Thyroid Nodule Segmentation with YOLOv5 Instance Segmentation

摘要

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