Abstract 4932: UltiAnalyzer.AI: An automatic and robust AI-Driven tool for the quantification of multiplex immunofluorescence whole slide images
Authors: Cardenes Ruben , Wood Douglas , Schulze Martin , Lee Je , Rognoni Lorenz 
Published: March 2024

Abstract
Introduction: Whole slide multiplexed immunofluorescence (mIF) facilitates in-depth study of the tumor microenvironment with multi-channel images resulting in an enormous amount of protein expression and spatial data across millions of single cells. Therefore, it has become crucial to process this huge amount of data automatically and reliably. Here we introduce UltiAnalyzer.AI, a software tool that integrates a set of artificial intelligence (AI) models specifically designed for the analysis of whole slide mIF slides, that is fully automatic, highly accurate, highly efficient, and fully scalable.
Methods: UltiAnalyzer.AI processes are specialized for 1) tissue segmentation, in which tissue regions are identified using a semantic segmentation AI model on low magnification versions of the DAPI channel, 2) tiling, where slide images are divided into 512x512 overlapping tiles for efficient image processing and lower memory requirements, 3) tumor region segmentation based on a semantic segmentation AI model, 4) nucleus segmentation using the open-source StarDist model, and 5) marker-positive cell detection, in which AI object detection models are employed to classify nuclei as positive or negative in each marker channel using the Faster R-CNN architecture. Here, we take advantage of the full 16-bit dynamic range of images produced using Ultivue InSituPlex® amplification technology, although our approach could be applied to other platforms. Intensity thresholding is no longer necessary, and identification of a positive cell mainly considers the morphology of the cell in the context of its cellular neighborhood.
Results: Trained on over 60 mIF slides and validated with manual annotations, UltiAnalyzer.AI has demonstrated strong performance, achieving f1-scores between 0.8 to 0.91 for cell detection, and average correlations of 0.9 for cell counts across 15 markers. Processing time is around 3s per tile, enabling the processing of large slides in under an hour. It has been tested on over 1000 slides, and in a comparative study, it matched a VisioPharm-based quantification with 0.95 correlation in positive counts for 8 markers. Cloud deployment allows for processing thousands of slides simultaneously and in considerably less time.
Conclusions: UltiAnalyzer.AI represents a significant advancement in the field of whole-slide mIF analysis, offering high accuracy and scalability using fully automated processes. By utilizing the full dynamic range of Ultivue’s mIF technology and avoiding traditional thresholding methods, the tool also increases robustness and reproducibility. The incorporation of cutting-edge AI technology and scalable cloud computing capabilities positions UltiAnalyzer.AI as a leading-edge solution for mIF analysis in digital pathology.
Citation Format: Ruben Cardenes, Douglas Wood, Martin Schulze, Je Lee, Lorenz Rognoni. UltiAnalyzer.AI: An automatic and robust AI-Driven tool for the quantification of multiplex immunofluorescence whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4932.