PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit

• URL: http://arxiv.org/abs/2501.06151v1
• Date: Fri, 10 Jan 2025 18:24:00 GMT
• Title: PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit
• Authors: Yuechen Yang, Yu Wang, Tianyuan Yao, Ruining Deng, Mengmeng Yin, Shilin Zhao, Haichun Yang, Yuankai Huo,
• Abstract summary: We present PySpatial, a high-speed pathomics toolkit specifically for WSI-level analysis.<n> PySpatial streamlines the conventional pipeline by directly operating on computational regions of interest.<n>Our experiments on two datasets-Perivascular Epithelioid Cell (PEC) and data from the Kidney Precision Medicine Project (KPMP)-demonstrate substantial performance improvements.
• Score: 5.52658544303762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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