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

全球甲狀腺癌發病率持續上升，推動了各類計算機輔助檢測技術的發展。精準分割甲狀腺結節是構建AI輔助臨床決策支持系統的關鍵第一步。本研究基於超聲影像，採用YOLOv5算法實現甲狀腺結節的實例分割。我們在包含與不包含多普勒圖像的兩種數據集版本上，評估了五種YOLOv5變體（Nano、Small、Medium、Large和XLarge）。結果顯示，YOLOv5-Large算法在包含多普勒圖像的數據集上表現最佳，其Dice相似係數達91%，平均精度均值(mAP)為0.87。值得注意的是，通常被醫師排除的多普勒圖像能顯著提升分割性能：當排除多普勒圖像時，YOLOv5-Small模型的Dice係數為79%，而包含多普勒圖像後所有模型變體性能均獲提升。這些發現表明，基於YOLOv5的實例分割技術可為甲狀腺結節檢測提供高效的實時解決方案，在自動化診斷系統中具有臨床應用潛力。

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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