Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy

Light-sheet fluorescence microscopy (LSFM) is a minimally invasive and
high-throughput imaging technique ideal for capturing large volumes of
tissue with sub-cellular resolution. A fundamental requirement for LSFM is
a seamless overlap of the light-sheet that excites a selective plane in
the specimen, with the focal plane of the objective lens. However, spatial
heterogeneity in the refractive index of the specimen often results in
violation of this requirement when imaging deep in the tissue. To address
this issue, autofocus methods are commonly used to refocus the focal plane
of the objective lens on the light-sheet. Yet, autofocus techniques are
slow since they require capturing a stack of images and tend to fail in
the presence of spherical aberrations that dominate volume imaging. To
address these issues, we present a deep learning-based autofocus framework
that can estimate the position of the objective-lens focal plane relative
to the light-sheet, based on two defocused images. This approach
outperforms or provides comparable results with the best traditional
autofocus method on small and large image patches, respectively. When the
trained network is integrated with a custom-built LSFM, a certainty
measure is used to further refine the network’s prediction. The network
performance is demonstrated in real-time on cleared genetically labeled
mouse forebrain and pig cochleae samples. Our study provides a framework
that could improve light-sheet microscopy and its application toward
imaging large 3D specimens with high spatial resolution.