λSplit: Self-Supervised Content-Aware Spectral Unmixing for Fluorescence Microscopy

In fluorescence microscopy, spectral unmixing aims to re-cover individual fluorophore concentrations from spectral images thatcapture mixed fluorophore emissions. Since classical methods operatepixel-wise and rely on least-squares fitting, their performance degradeswith increasingly overlapping emission spectra and higher levels of noise,suggesting that a data-driven approach that can learn and utilize a struc-tural prior might lead to improved results. Learning-based approachesfor spectral imaging do exist, but they are either not optimized for mi-croscopy data or are developed for very specific cases that are not ap-plicable to fluorescence microscopy settings. To address this, we pro-pose λSplit, a physics-informed deep generative model that learns aconditional distribution over concentration maps using a hierarchicalVariational Autoencoder. A fully differentiable Spectral Mixer enforcesconsistency with the image formation process, while the learned struc-tural priors enable state-of-the-art unmixing and implicit noise removal.We demonstrate λSplit on 3 real-world datasets that we syntheticallycast into a total of 58 challenging spectral unmixing benchmarks. Wecompare our results against a total of 10 baseline methods, includingclassical methods and a range of learning-based methods. Our resultsconsistently show competitive performance and improved robustness inhigh noise regimes, when spectra overlap considerably, or when the spec-tral dimensionality is lowered, making λSplit a new state-of-the-art forspectral unmixing of fluorescence microscopy data. Importantly, λSplitis compatible with spectral data produced by standard confocal micro-scopes, enabling immediate adoption without specialized hardware mod-ifications. The code and all datasets used in the experiments are availableat https://github.com/juglab/lambdaSplit.