MxIF Q-score: Biology-Informed Quality Assurance for Multiplexed Immunofluorescence Imaging

Abstract

Medical image processing and analysis on whole slide imaging (WSI) are notoriously difficult due to its giga-pixel high-resolution nature. Multiplex immunofluorescence (MxIF), a spatial single-cell level iterative imaging technique that collects dozens of WSIs on the same histological tissue, makes the data analysis an order of magnitude more complicated. The rigor of downstream single-cell analyses (e.g., cell type annotation) depends on the quality of the image processing (e.g., multi-WSI alignment and cell segmentation). Unfortunately, the high-resolutional and high-dimensional nature of MxIF data prevent the researchers from performing comprehensive data curations manually, thus leads to misleading biological findings. In this paper, we propose a learning based MxIF quality score (MxIF Q-score) that integrates automatic image segmentation and single-cell clustering methods to conduct biology-informed MxIF image data curation. To the best of our knowledge, this is the first study to provide an automatic quality assurance score of MxIF image alignment and segmentation from an automatic and biological knowledge-informed standpoint. The proposed method was validated on 245 MxIF image regions of interest (ROIs) from 49 WSIs and achieved 0.99 recall and 0.86 precision when compared with manual visual check on spatial alignment validation. We present extensive experimental results to show the efficacy of the Q-score system. We conclude that a biological knowledge driven scoring framework is a promising direction of assessing the complicated MxIF data.