Quantifying the phagocytosis of dynamic, unstained cells is essential for
evaluating neurodegenerative diseases. However, measuring rapid cell
interactions and distinguishing cells from backgrounds make this task
challenging when processing time-lapse phase-contrast video microscopy. In this
study, we introduce a fully automated, scalable, and versatile realtime
framework for quantifying and analyzing phagocytic activity. Our proposed
pipeline can process large data-sets and includes a data quality verification
module to counteract potential perturbations such as microscope movements and
frame blurring. We also propose an explainable cell segmentation module to
improve the interpretability of deep learning methods compared to black-box
algorithms. This includes two interpretable deep learning capabilities: visual
explanation and model simplification. We demonstrate that interpretability in
deep learning is not the opposite of high performance, but rather provides
essential deep learning algorithm optimization insights and solutions.
Incorporating interpretable modules results in an efficient architecture design
and optimized execution time. We apply this pipeline to quantify and analyze
microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain
statistically reliable results showing that FTD mutant cells are larger and
more aggressive than control cells. To stimulate translational approaches and
future research, we release an open-source pipeline and a unique microglial
cells phagocytosis dataset for immune system characterization in
neurodegenerative diseases research. This pipeline and dataset will
consistently crystallize future advances in this field, promoting the
development of efficient and effective interpretable algorithms dedicated to
this critical domain. this https URL

本研究提出了一种全自动、可扩展的实时框架，用于定量和分析噬菌作用，使用可解释的深度学习模块以提高深度学习方法的可解释性，并将其应用于前颞叶痴呆症（FTD）中微胶质细胞噬菌作用的量化和分析，得出了将 FTD 变异细胞与对照组细胞相比较具有更大和更具侵略性的结果。