Abstract

Abstract BACKGROUND CODEX (CO-Detection by indEXing) imaging has emerged as a pivotal tool in cellular biology, providing unprecedented insights into cellular distributions and interactions within the tissue microenvironment, crucial for advancing computational pathology. Despite its advantages, CODEX is susceptible to stitching errors during the merging of image tiles, which can significantly distort spatial analysis by introducing artifacts that obscure true cellular architecture. METHODS Addressing this challenge, we introduce CISD (CODEX Image Stitch Detection), a novel, user-friendly algorithm optimized for the detection of stitching artifacts in large-scale CODEX datasets. Developed and validated using a comprehensive dataset from 7 matched glioblastoma patients—encompassing over 2800 images across 29 immune protein markers—CISD utilizes a combination of image preprocessing, statistical measures, and thresholding to detect stitching errors. RESULTS CISD demonstrates robust performance, achieving a median Area Under Curve (AUC) value in the gold standard dataset where 73% of markers exceed 70% AUC, and 17% surpass 90%. Further validation shows that in a secondary dataset, 45% of markers maintain AUC values over 70% with 38% exceeding 90%, validating the algorithm’s consistent efficacy across varied datasets. CONCLUSION CISD’s significance lies not only in its computational achievements but also in its accessibility, facilitating high-quality analysis of extensive datasets without complex pattern recognition requirements. This democratizes high-precision computational pathology, enabling more researchers to accurately analyze spatial data. By reducing technical barriers, CISD sets a new benchmark in CODEX imaging, enhancing data reliability and accelerating research discoveries in cellular biology. This innovation stands to substantially refine spatial analysis in digital pathology, ensuring dependable interpretation of complex biological data.