2024/08/14 by Toru Nagasaka, Kimihiro Yamashita, Nagasaka, Toru +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · #Artificial Intelligence (cs.AI) #Cell Image Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #Image Processing Techniques and Applications #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2408.16008
openalex publication_date 2024/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The study presents a novel approach for quantifying cellular interactions in digital pathology using deep learning-based image cytometry. Traditional methods struggle with the diversity and heterogeneity of cells within tissues. To address this, we introduce the Spatial Interaction Potential (SIP) and the Co-Localization Index (CLI), leveraging deep learning classification probabilities. SIP assesses the potential for cell-to-cell interactions, similar to an electric field, while CLI incorporates distances between cells, accounting for dynamic cell movements. Our approach enhances traditional methods, providing a more sophisticated analysis of cellular interactions. We validate SIP and CLI through simulations and apply them to colorectal cancer specimens, demonstrating strong correlations with actual biological data. This innovative method offers significant improvements in understanding cellular interactions and has potential applications in various fields of digital pathology.