标题： PySpatial：一个高速全切片图像病理组学工具包 显示英文标题

标题： PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit

摘要： 全切片图像（WSI）分析在现代数字病理学中起着关键作用，使从组织样本中进行大规模特征提取成为可能。 然而，基于像CellProfiler这样的工具的传统特征提取流程通常涉及冗长的工作流程，需要将WSI分割成块，然后在块级别进行特征提取，并随后映射回原始WSI。 为了解决这些挑战，我们提出了PySpatial，这是一个专为WSI级分析设计的高速路径组学工具包。 PySpatial通过直接在计算感兴趣区域上操作，简化了传统流程，减少了冗余处理步骤。 利用基于rtree的空间索引和基于矩阵的计算，PySpatial高效地映射和处理计算区域，在保持高精度的同时显著加速了特征提取。 我们在两个数据集-周围血管上皮细胞（PEC）和肾脏精准医学项目（KPMP）的数据-上的实验表明了性能的显著提升。 对于PEC数据集中较小且稀疏的物体，PySpatial相比标准的CellProfiler流程几乎提升了10倍的速度。 对于KPMP数据集中的较大物体，如肾小球和动脉，PySpatial提升了2倍的速度。 这些结果突显了PySpatial在提高效率和准确性方面处理大规模WSI分析的潜力，为数字病理学的更广泛应用铺平了道路。 显示英文摘要

摘要： Whole Slide Image (WSI) analysis plays a crucial role in modern digital pathology, enabling large-scale feature extraction from tissue samples. However, traditional feature extraction pipelines based on tools like CellProfiler often involve lengthy workflows, requiring WSI segmentation into patches, feature extraction at the patch level, and subsequent mapping back to the original WSI. To address these challenges, we present PySpatial, a high-speed pathomics toolkit specifically designed for WSI-level analysis. PySpatial streamlines the conventional pipeline by directly operating on computational regions of interest, reducing redundant processing steps. Utilizing rtree-based spatial indexing and matrix-based computation, PySpatial efficiently maps and processes computational regions, significantly accelerating feature extraction while maintaining high accuracy. Our experiments on two datasets-Perivascular Epithelioid Cell (PEC) and data from the Kidney Precision Medicine Project (KPMP)-demonstrate substantial performance improvements. For smaller and sparse objects in PEC datasets, PySpatial achieves nearly a 10-fold speedup compared to standard CellProfiler pipelines. For larger objects, such as glomeruli and arteries in KPMP datasets, PySpatial achieves a 2-fold speedup. These results highlight PySpatial's potential to handle large-scale WSI analysis with enhanced efficiency and accuracy, paving the way for broader applications in digital pathology.