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Spinning disk confocal microscopy enables fast optical sectioning with low phototoxicity but is often inaccessible due to high hardware costs. We present a lower-cost solution using a 25-megapixel machine vision CMOS camera and a custom-built spinning disk. This camera uses a back-illuminated sensor with high quantum efficiency and low read noise. High-resolution images of Thy1-GFP mouse brain slices, Drosophila embryos and larvae, and H&E-stained rat testis verified performance across 3D tissue volumes. The measured resolution was 215.8 nm in X, Y and 521.9 nm in Z with a 60×/1.42 NA objective. The custom disk, made with 18 µm pinholes (180 µm pitch) on a chrome photomask and mounted to an optical chopper motor, enables stable, near-telecentric imaging at lower magnifications. Micromanager software integration allows synchronized control of all hardware, which demonstrates that affordable CMOS sensors can potentially replace sCMOS in spinning disk microscopy, offering an open-access, scalable solution for advanced imaging.

Correlative microscopy techniques are used for many different applications in the biological sciences because the comparison of different imaging methods allows researchers to gain more insight and data from samples. Correlative light and electron microscopy (CLEM) methods have been developed to preserve biological samples to withstand the harsh environments necessary for electron microscopy. After first being imaged using widefield (WF) and super-resolution structured illumination fluorescence microscopy (SIM), a NanoSuit chemical treatment was applied to a mammalian testis sample before imaging with scanning electron microscopy (SEM). This was done to compare the image quality and resolution of each technique. SEM yields higher resolution and offers validation of results from SIM.

Live-cell fluorescence microscopy enables the study of dynamic cellular processes. However, fluorescence microscopy can damage cells and disrupt these dynamic processes through photobleaching and phototoxicity. Reducing light exposure mitigates the effects of photobleaching and phototoxicity but results in low signal-to-noise ratio (SNR) images. Deep learning provides a solution for restoring these low-SNR images. However, these deep learning methods require large, representative datasets for training, testing, and benchmarking, as well as substantial GPU memory, particularly for denoising large images. We present a new fluorescence microscopy dataset designed to expand the range of imaging conditions and specimens currently available for evaluating denoising methods. The dataset contains 324 paired high/low-SNR images ranging from four to 282 megapixels across 12 sub-datasets that vary in specimen, objective used, staining type, excitation wavelength, and exposure time. The dataset also includes spinning disk confocal microscopy examples and extreme-noise cases. We evaluated three state-of-the-art deep learning denoising models on the dataset: a supervised transformer-based model, a supervised CNN model, and an unsupervised single image model. We also developed an image stitching method that enables large images to be processed in smaller crops and reconstructed. Our dataset provides a diverse benchmark for evaluating deep learning denoising methods, and our stitching method provides a solution to GPU memory constraints encountered when processing large images. Among the evaluated deep learning models, the supervised transformer-based model had the highest denoising performance but required the longest training time.

Spinning disk confocal microscopy enables fast optical sectioning with low phototoxicity but is often inaccessible due to high hardware costs. We present a low-cost solution using a 25 megapixel machine vision CMOS camera (Sony IMX540, FLIR Blackfly S) and a custom-built spinning disk. The system uses a back-illuminated sensor with high quantum efficiency (69% at 525 nm) and low read noise (2.31 electrons). High-resolution images of Thy1-GFP mouse brain slices and H&E-stained rat testis verified performance across 3D tissue volumes. The custom disk, made with 18 μm pinholes (180 μm pitch) on a chrome photomask and mounted to an optical chopper motor, enables stable, near-telecentric imaging. Micro-Manager software integration allows synchronized control of all hardware, which demonstrates that affordable CMOS sensors can potentially replace sCMOS in spinning disk microscopy, offering an open-source, scalable solution for advanced imaging.