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Dark-field microscopy is known to offer both high resolution and direct visualization of thin samples. However, its performance and optimization on thick samples is under-explored and so far, only mesoscale information from whole organisms has been demonstrated. In this work, we carefully investigate the difference between trans-and epi-illumination configurations. Our findings suggest that the epi-illumination configuration is superior in both contrast and fidelity compared to trans-illumination, while having the added advantage of experimental simplicity and an "open top" for experimental intervention. Guided by the theoretical analysis, we constructed an epi-illumination dark-field microscope with measured lateral and axial resolutions of 260 nm and 520 nm, respectively. Subcellular structures in whole organisms were directly visualized without the need for image reconstruction, and further confirmed via simultaneous fluorescence imaging. With an imaging speed of 20 to 50 fps, we visualize fast dynamic processes such as cell division and pharyngeal pumping in Caenorhabditis elegans.dark-field microscope, label free, optical sectioning, small organisms | INTRODUCTIONWith the advent of modern gene-editing techniques, human physiological and pathological processes can be simulated and studied in model organisms with small and tractable genomes [1,2]. In order to study and connect behavior and disease with the underlying physiological and pathological pathways, often multi-modal, multiscale information including both labeled and nonlabeled images at both micro-and meso-scales are desired.

Lipid droplets are the major organelles for fat storage in a cell and analyzing lipid droplets in Caenorhabditis elegans (C. elegans) can shed light on obesity-related diseases in humans. In this work, we propose to use a label free scattering-based method, namely dark field microscopy, to visualize the lipid droplets with high contrast, followed by deep learning to perform automatic segmentation. Our method works through combining epi-illumination dark field microscopy, which provides high spatial resolution, with asymmetric illumination, which computationally rejects multiple scattering. Due to the raw data’s high quality, only 25 images are required to train a Convolutional Neural Network (CNN) to successfully segment lipid droplets in dense regions of the worm. The performance is validated on both healthy worms as well as those in starvation conditions, which alter the size and abundance of lipid droplets. Asymmetric illumination substantially improves CNN accuracy compared with standard dark field imaging from 70% to be 85%, respectively. Meanwhile, standard segmentation methods such as watershed and DIC object tracking (DICOT) failed to segment droplets due to the images’ complex label-free background. By successfully analyzing lipid droplets in vivo and without staining, our method liberates researchers from dependence on genetically modified strains. Further, due to the “open top” of our epi-illumination microscope, our method can be naturally integrated with microfluidic chips to perform large scale and automatic analysis.