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

Abstract

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.

Super-Resolution Microscopy: Shedding New Light on In Vivo Imaging

“…Reprinted from Corsetti et al (2020) with permission from Optical Society of America. (E) Overview of the integration of the deep learning-based autofocus method with a custom-built LSM ( Li et al, 2021 ). Reprinted from Li et al (2021) with permission from Optical Society of America.…”

“…The adoption of deep-learning in their system has demonstrated a near two-fold improvement in resolution whilst maintaining a wide field of view for light sheet imaging. Li et al introduced a deep learning-based autofocus framework that can estimate the position of the objective-lens focal plane relative to the light-sheet ( Figure 4E ) ( Li et al, 2021 ). They realized a large 3D specimens imaging with high spatial resolution.…”

Ontogeny of cellular organization and LGR5 expression in porcine cochlea revealed using tissue clearing and 3D imaging

“…Clearing, labeling, and imaging the mature cochleae of pigs required resection from the surrounding bone and an optimized tissue-clearing protocol. The BoneClear process was used to render the tissue transparent ( Wang et al., 2019 ), and the 3D volume of the specimen was imaged using a custom adaptive light-sheet microscope, as described in our previous works ( Li et al., 2021a ; Moatti et al., 2020 ). The custom adaptive light-sheet microscope can provide excellent optical sectioning capabilities while maintaining a fast acquisition rate ( Santi et al., 2009 ).…”

Robust autofocusing for scanning electron microscopy based on a dual deep learning network

“…Luo, et al 11 and Na, et al 12 respectively proposed an autofocus method for optical microscopy and SEM that receives a defocused image as an input and outputs a virtual focused image based on deep learning. Li, et al 13 developed a deep learning model that estimates the appropriate objective lens position by receiving two defocused images for light-sheet fluorescence microscopy. In addition, Jang, et al 14 attempted autofocus SEM based on deep reinforcement learning using SEM parameters.…”

“…In addition, SEM is based on electron beams instead of light, making it much more vulnerable to noise than optical microscopes due to factors such as electron absorption properties and charge-damaged artifacts 23 , 24 . Furthermore, deep learning-based autofocus research is also difficult to be widely applied to SEM, because most of the studies have been applied to optical microscopes based on numerical image quality metric, which have the aforementioned limitations 11 , 13 . Even when applied to SEM, there are limitations in the range of available WD and magnification 12 , 14 .…”