Spatial Analysis for AI-segmented Histopathology Images: Methods and Implementation

Quantitative characterization of cellular spatial organization is critical for understanding tumor progression and immune response. Recent advances in artificial intelligence (AI) enable large-scale segmentation and classification of nuclei from digitized histopathology slides, producing massive point pattern and marked point pattern data. However, accessible and standardized tools for downstream spatial statistical analysis remain limited. We present SASHIMI (Spatial Analysis for Segmented Histopathology Images using Machine Intelligence), a browser-based platform for real-time spatial analysis of AI-segmented histopathology images. Rather than proposing new spatial methods, SASHIMI systematically organizes and operationalizes 27 widely used spatial summary statistics, areal indices, and topological features within a unified computational framework. The platform computes mathematically grounded descriptors including K-, L-, G-, F-, and J-functions, pair correlation and mark connection functions, spatial autocorrelation measures, similarity indices, and persistent homology-based topological summaries. Outputs include both functional curves and scalar feature tables suitable for downstream statistical modeling. We illustrate the framework using two cancer cohorts: oral potentially malignant disorders and non-small-cell lung cancer. Across datasets, cross-type spatial interactions and topological descriptors show associations with patient survival, demonstrating that complementary spatial features capture distinct aspects of tumor microenvironment architecture. SASHIMI provides an accessible, reproducible platform for single-cell-level spatial profiling of tumor tissue, enabling interactive visualization and standardized feature extraction without requiring programming expertise.