Asymmetrical Illumination Enables Lipid Droplets Segmentation in Caenorhabditis elegans Using Epi-Illumination Dark Field Microscopy

Abstract

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.