使用YOLOv11,StarDist和SAM2在整个幻灯片图像中进行精确细胞细分的混合深度学习框架
Julius Bamwenda1, Mehmet Siraç Özerdem1, Orhan Ayyıldız2
1Engineering Faculty, Electrical & Electronics Engineering Department, Dicle University, 21280 Diyarbakır, Türkiye.
Bioengineering (Basel, Switzerland)
|June 26, 2025
概括
本研究介绍了一种混合深度学习框架,用于在整个幻灯片图像 (WSIs) 中精确的细胞细分. 它结合了YOLOv11,StarDist和Segment Anything Model v2 (SAM2),显著提高了计算病理学应用的准确性.
科学领域:
- 计算病理学计算病理学
- 医学图像分析 医学图像分析
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 在整个幻灯片图像 (WSIs) 中精确的细胞细分对于计算病理学的定量分析至关重要.
- 传统方法面临的挑战是由于WSIs的复杂性和规模.
研究的目的:
- 开发一种新的混合深度学习框架,用于在WSIs中进行强大而精确的细胞细分.
- 为了集成YOLOv11,StarDist和SegmentAnything Model v2 (SAM2) 以提高性能.
主要方法:
- 这是一个混合框架,它结合了YOLOv11用于对象检测,StarDist用于精确的边界建模,以及SAM2用于提示指导的细分.
- 使用YOLOv11输出作为提示或过器SAM2和StarDist在密集地区的几何精度.
- 在WSI数据集上评估,具有高分辨率的细胞层面面具.
主要成果:
- 拟议的混合方法在细胞细分方面明显优于单个基线模型.
- 在各种组织类型中实现了增强的边界精度,改进的局部化和更大的强度.
- 定量评估显示使用子系数,IOU,F1得分,精度和回忆的性能优越.
结论:
- 综合框架提供了一个可扩展和模块化解决方案,用于自动化基因病理图像分析.
- 混合深度学习方法可以克服复杂的WSI细分单个模型的局限性.
- 这种方法通过提高细胞细分精度,在计算病理学中推进了定量分析.
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