bibtext: @InProceedings{ Gau_Immuneguided_MICCAI2024,
                   author = { Gautam, Tanishq and Gonzalez, Karina P. and Salvatierra, Maria E. and Serrano, Alejandra and Chen, Pingjun and Pan, Xiaoxi and Shokrollahi, Yasin and Ranjbar, Sara and Rodriguez, Leticia and Team, Patient Mosaic and Solis-Soto, Luisa and Yuan, Yinyin and Castillo, Simon P. },
                   title = { { Immune-guided AI for Reproducible Regions of Interest Selection in Multiplex Immunofluorescence Pathology Imaging } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: Selecting regions of interest (ROIs) in whole-slide histology images (WSIs) is a crucial step for spatial molecular profiling. As a general practice, pathologists manually select ROIs within each WSI based on morphological tumor markers to guide spatial profiling, which can be inconsistent and subjective. To enhance reproducibility and avoid inter-pathologist variability, we introduce a novel immune-guided end-to-end pipeline to automate the ROI selection in multiplex immunofluorescence (mIF) WSIs stained with three cell markers (Syto13, CD45, PanCK). First, we estimate immune infiltration (CD45+ expression) scores at the grid level in each WSI. Then, we incorporate the Pathology Language and Image Pre-Training (PLIP) foundational model to extract features from each grid and further select a subset of grids representative of the whole slide that comparatively matches pathologists’ assessment. Further, we implement state-of-the-art detection models for ROI detection in each grid, incorporating learning from pathologists’ ROI selection. Our study shows a significant correlation between our automated method and pathologists’ ROI selection across five different types of carcinomas, as evidenced by a significant Spearman’s correlation coefficient (> 0.785, p < 0.001), substantial inter-rater agreement (Cohen’s kappa > 0.671), and the ability to replicate the ROI selection made by independent pathologists with excellent average performance (0.968 precision and 0.991 mean average precision at a 0.5 intersection-over-union). By minimizing manual intervention, our solution provides a flexible framework that potentially adapts to various markers, thus enhancing the efficiency and accuracy of digital pathology analyses.
title: Immune-guided AI for Reproducible Regions of Interest Selection in Multiplex Immunofluorescence Pathology Imaging
authors: Gautam, Tanishq Gonzalez, Karina P. Salvatierra, Maria E. Serrano, Alejandra Chen, Pingjun Pan, Xiaoxi Shokrollahi, Yasin Ranjbar, Sara Rodriguez, Leticia Team, Patient Mosaic Solis-Soto, Luisa Yuan, Yinyin Castillo, Simon P.