Galinova, V. | Search bioRxiv

bioRxiv2025

MicroSplit: Semantic Unmixing of Fluorescent Microscopy Data

Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscopes optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce Micro[S]plit, a computational multiplexing technique based on deep learning that allows multiple cellular structures to be imaged in a single fluorescent channel and then unmix them by computational means, allowing faster imaging and reduced photon exposure. We show that Micro[S]plit efficiently separates up to four superimposed noisy structures into distinct denoised fluorescent image channels. Furthermore, using Variational Splitting Encoder-Decoder (VSE) networks, our approach can sample diverse predictions from a trained posterior of solutions. The diversity of these samples scales with the uncertainty in a given input, allowing us to estimate the true prediction errors by computing the variability between posterior samples. We demonstrate the robustness of Micro[S]plit networks, which are trained for each splitting task at hand, across various datasets and noise levels and show its utility to image more, to image faster, and to improve downstream analysis. We provide Micro[S]plit along with all associated training and evaluation datasets as open resources, enabling life scientists to immediately benefit from the potential of computational multiplexing and thus help accelerate the rate of their scientific discovery process.

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bioRxiv2024

FeatureForest: the power of foundation models, theusability of random forests

Once the work at the microscope is done, biological discoveries rely heavily on proper downstream analysis. This often amounts to first segmenting the biological objects of interest in the image before performing a quantitative analysis. Deep-learning (DL) is nowadays ubiquitous in such segmentation tasks. However, DL can be cumbersome to apply, as it often requires large amount of manual labeling to produce ground-truth data, and expert knowledge to train the models from scratch. Nonetheless, the performance of large foundation models, although trained on natural images, are improving on scientific images with every new model released. They, however, require either manual prompting or tedious post-processing to selectively segment the biological objects of interest. Classical machine learning algorithms, such as random forest classifiers, on the other hand, are well-established, easy to train, and often yield results of sufficient quality for downstream processing tasks, hence their continued popularity. Unfortunately, they are limited to objects with distinct, well-defined textures compared to their environment. This generally limits their usefulness to structures easy to recognize. Here, we present FeatureForest, an open-source tool that leverages the feature embeddings of large foundation models to train a random forest classifier, thereby providing users with a rapid way of semantically segmenting complex images using only a few labeling strokes. We demonstrate the improvement in performance over a variety of datasets, including large and complex volumetric electron microscopy stacks. Our implementation is available in napari, currently integrates four foundation models, and can easily be extended to any new model once they become available.

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