2/3/2025 | 3:30 PM - 5:30 PM | Regency A

Pyspatial: A high-speed whole slide image pathomics toolkit

Author(s)

Yuechen Yang | Department of Computer Science, Vanderbilt University, Nashville, TN, USA
Yu Wang
Tianyuan Yao | Department of Computer Science, Vanderbilt University, Nashville, TN, USA
Ruining Deng | Department of Computer Science, Vanderbilt University, Nashville, TN, USA
Mengmeng Yin
Shilin Zhao
Haichun Yang | Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
Yuankai Huo | Department of Computer Science, Vanderbilt University, Nashville, TN, USA

Abstract

In this paper, we present PySpatial, a new open-source pathomics toolkit optimized for Whole Slide Image (WSI) analysis. PySpatial addresses the challenges of processing super high-resolution histopathological data by efficiently differentiating giga-pixel images into computational and non-computational regions. This approach not only enhances computational speed but also minimizes memory usage by focusing primarily on the essential computational areas. A novel data structure, the Computational Region Pool (CRP), has been developed to efficiently store and process only relevant image segments. By offering PySpatial as an open-source resource, we aim to empower researchers to perform WSI-level pathomic analyses more effectively and accurately.