在甲状腺超声波成像中识别病变位置并区分良性和恶性发现
Xu Yang1, Hongliang Geng1, Xue Wang2
1School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China.
Scientific reports
|December 31, 2024
概括
这项研究引入了一个AI框架,用于精确地检测甲状腺结节,使用YOLOv8在超声波图像上. 这种新方法显著提高了良性和恶性结节的诊断率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 甲状腺结节很普遍,超声波诊断是主观的,有误诊的风险.
- 医生的经验显著影响了基于超声波的甲状腺结节检测准确度.
研究的目的:
- 开发一个使用YOLOv8.8的甲状腺结节自动检测和分类框架.
- 通过超声波图像提高甲状腺结节诊断的准确性和可靠性.
主要方法:
- 一个端到端的框架,整合了超声波图像的灰度和弹性特征.
- 采用了YOLOv8与注意力加权可变形卷积网络 (DCN) 进行特征提取.
- 整合了跨阶段的部分通道注意力 (CPCA) 机制和功能融合网络,以改善学习.
主要成果:
- 取得的诊断率为良性甲状腺结核的89.3%,恶性甲状腺结核的90.4%.
- 在IoU=0.5 (mAP@50) 获得了95.5%的平均平均精度.
- 在611张临床超声波图像上,与基线模型相比,表现出6.6%的性能改善.
结论:
- 拟议的基于YOLOv8的框架提供准确有效的甲状腺结节的自动检测和分类.
- 集成先进的特征提取和融合技术提高了诊断性能,解决了手动解释的局限性.
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