Publications by authors named "Anthony Bilodeau"
Article Synopsis
- The integration of AI into microscopy greatly improves image acquisition and analysis, particularly in super-resolution microscopy.
- The development of AI-assisted microscopy is hampered by the lack of extensive biological datasets and challenges in standardizing methods across varied samples.
- The pySTED platform provides a realistic simulation environment that aids in training AI models and optimizing microscopy strategies, demonstrating successful application on actual systems without needing extensive adjustments.
Article Synopsis
- Super-resolution fluorescence microscopy is key for studying nanostructures in biological tissues, but it involves a balance of competing factors like resolution and light exposure.
- The task-assisted generative adversarial network (TA-GAN) enhances image resolution and quality by incorporating related tasks like segmentation while processing images from various microscopy techniques.
- By integrating TA-GAN into the microscopy pipeline, it optimizes image acquisition by predicting nanometric details, reducing light exposure, and allowing for better observation of dynamic molecular processes.
Article Synopsis
- Developing automated quantitative image analysis pipelines requires careful planning to ensure consistent and meaningful data extraction.
- Traditional methods rely on predefined rules for data extraction, but Machine/Deep Learning (ML/DL) can automate this process, enhancing tasks like segmentation and classification.
- The text outlines essential terms, steps for creating effective segmentation pipelines, and important technical considerations for building reliable automated image analysis using ML/DL.
The organization of proteins in the apposed nanodomains of pre- and postsynaptic compartments is thought to play a pivotal role in synaptic strength and plasticity. As such, the alignment between pre- and postsynaptic proteins may regulate, for example, the rate of presynaptic release or the strength of postsynaptic signaling. However, the analysis of these structures has mainly been restricted to subsets of synapses, providing a limited view of the diversity of synaptic protein cluster remodeling during synaptic plasticity.
View Article and Find Full Text PDFThe nanoscale organization of the F-actin cytoskeleton in neurons comprises membrane-associated periodical rings, bundles, and longitudinal fibers. The F-actin rings have been observed predominantly in axons but only sporadically in dendrites, where fluorescence nanoscopy reveals various patterns of F-actin arranged in mixed patches. These complex dendritic F-actin patterns pose a challenge for investigating quantitatively their regulatory mechanisms.
View Article and Find Full Text PDFTraditional approaches for finding well-performing parameterizations of complex imaging systems, such as super-resolution microscopes rely on an extensive exploration phase over the illumination and acquisition settings, prior to the imaging task. This strategy suffers from several issues: it requires a large amount of parameter configurations to be evaluated, it leads to discrepancies between well-performing parameters in the exploration phase and imaging task, and it results in a waste of time and resources given that optimization and final imaging tasks are conducted separately. Here we show that a fully automated, machine learning-based system can conduct imaging parameter optimization toward a trade-off between several objectives, simultaneously to the imaging task.
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