在数字病理学和空间奥米克学中,HistoSweep可用于千兆像素图像的细胞分辨率组织质量控制
bioRxiv : the preprint server for biology
|February 12, 2026
概括
HistoSweep是一个新的框架,用于清理高分辨率的组织学图像. 它消除了文物和背景噪声,提高了数字病理学和空间奥米克分析的准确性.
科学领域:
- 数字病理学数字病理学
- 空间奥米克斯空间奥米克斯
- 计算生物学是一种计算生物学.
背景情况:
- 高分辨率的组织学图像对于病理学和空间奥米学至关重要.
- 全幻灯片图像 (WSIs) 通常含有文物和背景噪音,妨碍分析.
- 目前的质量控制 (QC) 工具提供有限的细粒度的组织与非组织区域的分离.
研究的目的:
- 引入HistoSweep,这是一个可扩展的框架,用于在细胞分辨率下生成形态感知组织面具.
- 解决现有工具在WSIs细粒度质量控制中的局限性.
- 提高数字病理学和空间奥米克数据的可靠性和可重复性.
主要方法:
- HistoSweep集成了密度过,纹理描述器和自适应值.
- 该框架以细胞分辨率生成组织面具,区分信息组织与非信息区域.
- 它是为可扩展性而设计的,在标准CPU上高效地处理大型WSIs,而不需要GPU加速.
主要成果:
- HistoSweep系统地删除非信息区域,同时保留生物相关的微观结构.
- 与现有方法相比,该框架在25个不同的WSIs中表现出卓越的性能.
- 它改善了图像可视化,细分精度,虚拟细胞类型预测和空间转录学数据完整性.
结论:
- HistoSweep为数字病理学和空间奥米克学提供了一个基础的预处理步骤.
- 它可以实现细粒度,可扩展的质量控制,这对于准确的生物解释至关重要.
- 该工具提高了使用组织学图像进行计算分析的可靠性和可重复性.
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