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.
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Acknowledgements
This research was supported by the Leona M. and Harry B. Helmsley Charitable Trust grant G-1903-03793 and G-2103-05128, NSF CAREER 1452485, NSF 2040462, and in part using the resources of the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University. This project was supported in part by the National Center for Research Resources, Grant UL1 RR024975-01, and is now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06, the National Institute of Diabetes and Digestive and Kidney Diseases, the Department of Veterans Affairs I01BX004366, and I01CX002171. The de-identified imaging dataset(s) used for the analysis described were obtained from ImageVU, a research resource supported by the VICTR CTSA award (ULTR000445 from NCATS/NIH), Vanderbilt University Medical Center institutional funding and Patient-Centered Outcomes Research Institute (PCORI; contract CDRN-1306-04869). This work is supported by NIH grant T32GM007347 and grant R01DK103831.
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Bao, S. et al. (2022). MxIF Q-score: Biology-Informed Quality Assurance for Multiplexed Immunofluorescence Imaging. In: Huo, Y., Millis, B.A., Zhou, Y., Wang, X., Harrison, A.P., Xu, Z. (eds) Medical Optical Imaging and Virtual Microscopy Image Analysis. MOVI 2022. Lecture Notes in Computer Science, vol 13578. Springer, Cham. https://doi.org/10.1007/978-3-031-16961-8_5
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