# PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

arXiv: https://arxiv.org/abs/2304.13764 · HTML: https://mlsanity.com/paper/2304.13764/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 64 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 18 of 18)

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- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Deep Interactive Object Selection](https://mlsanity.com/paper/1603.04042/) (2016)
- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Deep Watershed Transform for Instance Segmentation](https://mlsanity.com/paper/1611.08303/) (2016)
- [BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture](https://mlsanity.com/paper/2007.00243/) (2020)
- [Microscopy Cell Segmentation via Convolutional LSTM Networks](https://mlsanity.com/paper/1805.11247/) (2018)
- [J Regularization Improves Imbalanced Multiclass Segmentation](https://mlsanity.com/paper/1910.09783/) (2019)
- [H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation](https://mlsanity.com/paper/2206.01309/) (2022)
- [NeuRegenerate: A Framework for Visualizing Neurodegeneration](https://mlsanity.com/paper/2202.01115/) (2022)

## Abstract (arXiv)

Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of DL methods compared to black-box algorithms. This includes two interpretable DL capabilities: visual explanation and model simplification. We demonstrate that interpretability in DL is not the opposite of high performance, by additionally providing essential DL algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply our pipeline to analyze microglial cell phagocytosis in FTD and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of interpretable algorithms dedicated to the domain of neurodegenerative diseases' characterization. github.com/ounissimehdi/PhagoStat