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物体检测作为一种帮助定位前列腺在基于表面的腹部超声波图像中的辅助工具
Rory D Bennett1, Tristan Barrett2, Vincent J Gnanapragasam3
1School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
Communications engineering
|November 29, 2025
概括
这项研究提出了一种新的前列腺检测框架,使用先进的物体检测模型,在超声波图像中实现高精度. 这项技术有助于前列腺癌风险分层,并减少不必要的活检.
科学领域:
- 医学成像医学成像
- 医疗保健中的人工智能
- 前列腺癌诊断的诊断方法
背景情况:
- 对象检测提高了医疗工作流程,有助于诊断和治疗计划.
- 前列腺癌风险分层利用前列腺特异性抗原密度和体积估计.
- 基于表面的超声波图像对精确的前列腺检测提出了挑战.
研究的目的:
- 引入使用最先进的物体检测模型的基线前列腺检测框架.
- 评估框架在挑战性超声波图像中检测前列腺的性能.
- 评估从检测结果中得出的前列腺体积估计的准确性.
主要方法:
- 实施基线物体检测框架用于前列腺识别.
- 5倍交叉验证以评估检测性能指标 (IoU,精度,回忆,F1,AP).
- 根据检测结果计算前列腺体积,并与地面真相测量相关.
主要成果:
- 对象检测模型成功地在困难的超声波图像中识别了前列腺.
- 实现过联交,精度,回忆,F1和平均精度值高于0.7.
- 前列腺体积估计显示与基本真相有很高的相关性 (皮尔森相关系数0.55-0.84).
- 演示了实时检测能力.
结论:
- 开发的框架有效地检测前列腺在超声波图像,使准确的体积估计.
- 这项技术支持实时前列腺癌风险分层工具的开发.
- 有潜力减少不必要的前列腺活检,提高医疗保健效率.
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