用YOLOv5结合彩超图像提升甲状腺结节分割精度
Doppler-Enhanced Deep Learning: Improving Thyroid Nodule Segmentation with YOLOv5 Instance Segmentation
- 基于YOLOv5的实例分割，融合彩超影像增强分割效果
- 引入彩超后模型Dice达91%，mAP达0.87，性能显著提升
- 适合临床医生与AI医疗系统开发者参考，尤其关注实时诊断
全球甲状腺癌发病率上升推动了多种辅助检测技术的发展。准确分割甲状腺结节是构建人工智能辅助临床决策系统的关键第一步。本研究采用YOLOv5算法对超声图像进行结节实例分割，评估了Nano、Small、Medium、Large和XLarge五个版本模型在含与不含多普勒图像的两个数据集上的表现。结果表明，包含多普勒图像时，YOLOv5-Large模型取得最高性能，Dice得分为91%，mAP为0.87。值得注意的是，尽管临床上常忽略多普勒图像，但其加入可显著提升所有模型的分割效果。相比之下，不使用多普勒图像时，YOLOv5-Small模型仅达到79%的Dice分数。研究证明，基于YOLOv5的实例分割能实现高效实时的甲状腺结节检测，具备潜在的临床自动化诊断应用价值。
原文摘要 · Abstract (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.
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