Flavie Lavoie-Cardinal

Paul De Koninck

2025-07-17

bioRxiv (preprint)

A Self-Supervised Foundation Model for Robust and Generalizable Representation Learning in STED Microscopy

Anthony Bilodeau

Frédéric Beaupré

Julia Chabbert

Kamylle Thériault

Andréanne Deschênes

Jean-Michel Bellavance

Koraly Lessard

Renaud Bernatchez

Paul De Koninck

Foundation Models (FMs) have dramatically increased the potential and power of deep learning algorithms through general capacities over a va… (see more)riety of tasks. The performance increase they offer is obtained without elaborated specific trainings for domains such as natural language processing and computer vision. However, their application in specialized fields like biomedical imaging and fluorescence microscopy remains difficult due to distribution shifts and the scarcity of high-quality annotated datasets. The high cost of data acquisition and the requirement for in-domain expertise further exacerbate this challenge in microscopy. To address this we introduce STED-FM, a foundation model specifically designed for super-resolution STimulated Emission Depletion (STED) microscopy. STED-FM leverages a Vision Transformer architecture trained at scale with Masked Autoencoding on a new dataset of nearly one million STED images. STED-FM learns expressive latent representations without requiring extensive annotations, yielding robust performance across diverse downstream microscopy image analysis tasks. Unsupervised experiments demonstrate the discriminative structure of its learned latent space. These representations can be leveraged for multiple downstream applications, including fully supervised classification and segmentation with reduced annotation requirements. Moreover, STED-FM representations enhance the performance of deep learning–based image denoising and improve the quality of images generated by diffusion models, enabling latent attribute manipulation for the data-driven discovery of subtle nanostructures and phenotypes, as well as algorithmic super-resolution. Moreover, its powerful structure retrieval capabilities are integrated into automated STED microscopy acquisition pipelines, paving the way for smart microscopy. In sum, we demonstrate that STED-FM lays a robust foundation for state-of-the-art algorithms across a wide array of tasks, establishing it as a highly valuable and scalable resource for researchers in super-resolution microscopy.

2025-06-05

bioRxiv (preprint)

Quantitative Analysis of Miniature Synaptic Calcium Transients Using Positive Unlabeled Deep Learning

Frédéric Beaupré

Anthony Bilodeau

Theresa Wiesner

Gabriel Leclerc

Mado Lemieux

Gabriel Nadeau

Katrine Castonguay

Bolin Fan

Simon Labrecque

Renée Hložek

Paul De Koninck

Ca 2+ imaging methods are widely used for studying cellular activity in the brain, allowing detailed ana… (see more)lysis of dynamic processes across various scales. Enhanced by high-contrast optical microscopy and fluorescent Ca 2+ sensors, this technique can be used to reveal localized Ca 2+ fluctuations within neurons, including in sub-cellular compartments, such as the dendritic shaft or spines. Despite advances in Ca 2+ sensors, the analysis of miniature Synaptic Calcium Transients (mSCTs), characterized by variability in morphology and low signal-to-noise ratios, remains challenging. Traditional threshold-based methods struggle with the detection and segmentation of these small, dynamic events. Deep learning (DL) approaches offer promising solutions but are limited by the need for large annotated datasets. Positive Unlabeled (PU) learning addresses this limitation by leveraging unlabeled instances to increase dataset size and enhance performance. This approach is particularly useful in the case of mSCTs that are scarce and small, associated with a very small proportion of the foreground pixels. PU learning significantly increases the effective size of the training dataset, improving model performance. Here, we present a PU learning-based strategy for detecting and segmenting mSCTs. We evaluate the performance of two 3D deep learning models, StarDist-3D and 3D U-Net, which are well established for the segmentation of small volumetric structures in microscopy datasets. By integrating PU learning, we enhance the 3D U-Net’s performance, demonstrating significant gains over traditional methods. This work pioneers the application of PU learning in Ca 2+ imaging analysis, offering a robust framework for mSCT detection and segmentation. We also demonstrate how this quantitative analysis pipeline can be used for subsequent mSCTs feature analysis. We characterize morphological and kinetic changes of mSCTs associated with the application of chemical long-term potentiation (cLTP) stimulation in cultured rat hippocampal neurons. Our data-driven approach shows that a cLTP-inducing stimulus leads to the emergence of new active dendritic regions and differently affects mSCTs subtypes.

2024-07-07

bioRxiv (preprint)

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Audrey Durand

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and… (see more) analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning.

2023-12-31

Nature Machine Intelligence (published)

Filtering Pixel Latent Variables for Unmixing Noisy and Undersampled Volumetric Images

Catherine Bouchard

Andréanne Deschênes

Vincent Boulanger

Jean-Michel Bellavance

Julia Chabbert

Alexy Pelletier-Rioux

2023-12-07

ArXiv (preprint)

arxiv.org

Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition

Catherine Bouchard

Theresa Wiesner

Andréanne Deschênes

Anthony Bilodeau

Benoit Turcotte

2023-07-26

Nature Machine Intelligence (published)

Filtering Pixel Latent Variables for Unmixing Volumetric Images

Catherine Bouchard

Vincent Boulanger

Measurements of different overlapping components require robust unmixing algorithms to convert the raw multi-dimensional measurements to use… (see more)ful unmixed images. Such algorithms perform reliable separation of the components when the raw signal is fully resolved and contains enough information to fit curves on the raw distributions. In experimental physics, measurements are often noisy, undersam-pled, or unresolved spatially or spectrally. We propose a novel method where bandpass filters are applied to the latent space of a multi-dimensional convolutional neural network to separate the overlapping signal components and extract each of their relative contributions. Simultaneously processing all dimensions with multi-dimensional convolution kernels empowers the network to combine the information from adjacent pixels and time-or spectral-bins, facilitating component separation in instances where individual pixels lack well-resolved information. We demonstrate the applicability of the method to real experimental physics problems using fluorescence lifetime microscopy and mode decomposition in optical fibers as test cases. The successful application of our approach to these two distinct experimental cases, characterized by different measured distributions, highlights the versatility of our approach in addressing a wide array of imaging tasks.

2022-12-31

arXiv.org (preprint)

Annotation Cost-Sensitive Deep Active Learning with Limited Data (Student Abstract)

Renaud Bernatchez

Audrey Durand

2022-06-27

Proceedings of the AAAI Conference on Artificial Intelligence (published)