Mehdi Ounissi

Gabriel Jimenez, Anuradha Kar, Mehdi Ounissi et al.

Quantifying the distribution and morphology of tau protein structures in brain tissues is key to diagnosing Alzheimer's Disease (AD) and its subtypes. Recently, deep learning (DL) models such as UNet have been successfully used for automatic segmentation of histopathological whole slide images (WSI) of biological tissues. In this study, we propose a DL-based methodology for semantic segmentation of tau lesions (i.e., neuritic plaques) in WSI of postmortem patients with AD. The state of the art in semantic segmentation of neuritic plaques in human WSI is very limited. Our study proposes a baseline able to generate a significant advantage for morphological analysis of these tauopathies for further stratification of AD patients. Essential discussions concerning biomarkers (ALZ50 versus AT8 tau antibodies), the imaging modality (different slide scanner resolutions), and the challenge of weak annotations are addressed within this seminal study. The analysis of the impact of context in plaque segmentation is important to understand the role of the micro-environment for reliable tau protein segmentation. In addition, by integrating visual interpretability, we are able to explain how the network focuses on a region of interest (ROI), giving additional insights to pathologists. Finally, the release of a new expert-annotated database and the code (\url{https://github.com/aramis-lab/miccai2022-stratifiad.git}) will be helpful for the scientific community to accelerate the development of new pipelines for human WSI processing in AD.

Mehdi Ounissi, Morwena Latouche, Daniel Racoceanu

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

Mehdi Ounissi, Ilias Sarbout, Jean-Pierre Hugot et al.

Chemical staining methods are dependable but require extensive time, expensive chemicals, and raise environmental concerns. These challenges highlight the need for alternative solutions like virtual staining, which accelerates the diagnostic process and enhances stain application flexibility. Generative AI technologies are pivotal in addressing these issues. However, the high-stakes nature of healthcare decisions, especially in computational pathology, complicates the adoption of these tools due to their opaque processes. Our work introduces the use of generative AI for virtual staining, aiming to enhance performance, trustworthiness, scalability, and adaptability in computational pathology. The methodology centers on a singular H&E encoder supporting multiple stain decoders. This design focuses on critical regions in the latent space of H&E, enabling precise synthetic stain generation. Our method, tested to generate 8 different stains from a single H&E slide, offers scalability by loading only necessary model components during production. We integrate label-free knowledge in training, using loss functions and regularization to minimize artifacts, thus improving paired/unpaired virtual staining accuracy. To build trust, we use real-time self-inspection with discriminators for each stain type, providing pathologists with confidence heat-maps. Automatic quality checks on new H&E slides ensure conformity to the trained distribution, ensuring accurate synthetic stains. Recognizing pathologists' challenges with new technologies, we have developed an open-source, cloud-based system, that allows easy virtual staining of H&E slides through a browser, addressing hardware/software issues and facilitating real-time user feedback. We also curated a novel dataset of 8 paired H&E/stains related to pediatric Crohn's disease, comprising 480 WSIs to further stimulate computational pathology research.