PySpatial：一种高速全切片图像病理组学工具包
PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit
AI总结：
本文提出面向 WSI 层级分析的高速病理组学工具包 PySpatial，利用 rtree 空间索引与矩阵计算直接处理感兴趣区域，在 PEC 和 KPMP 数据集上分别取得近 10 倍和 2 倍加速。
摘要：
全切片图像（WSI）分析在现代数字病理学中发挥着关键作用，能够从组织样本中进行大规模特征提取。然而，基于 CellProfiler 等工具的传统特征提取流程通常涉及冗长的工作流，需要将 WSI 分割为图像块、在图像块层级提取特征，随后再映射回原始 WSI。为应对这些挑战，我们提出 PySpatial，这是一种专为 WSI 层级分析设计的高速病理组学工具包。PySpatial 通过直接在计算感兴趣区域上操作来简化传统流程，减少冗余处理步骤。借助基于 rtree 的空间索引和基于矩阵的计算，PySpatial 能够高效映射并处理计算区域，在保持高精度的同时显著加快特征提取。我们在两个数据集——血管周上皮样细胞（PEC）数据集和来自肾脏精准医学项目（KPMP）的数据——上的实验证明了显著的性能提升。对于 PEC 数据集中较小且稀疏的对象，PySpatial 与标准 CellProfiler 流程相比实现了近 10 倍的加速。对于较大的对象，例如 KPMP 数据集中的肾小球和动脉，PySpatial 实现了 2 倍加速。这些结果凸显了 PySpatial 以更高效率和准确性处理大规模 WSI 分析的潜力，为其在数字病理学中更广泛的应用铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
