Urine sediment analysis utilizing quantitative phase imaging with digital holographic microscopy

We explored the capabilities of quantitative phase imaging (QPI) with digital holographic microscopy (DHM) for the characterization and classification of urine sediments. Bright-field images and off-axis holograms from a liquid control for urine analysis and human urine samples were acquired with a modular DHM system. From the retrieved images, particle morphology parameters were extracted by threshold and convolution neural network (CNN)-based segmentation procedures. Moreover, the ability of supervised machine learning algorithms to classify and identify urine sediment components based on biophysical parameters was evaluated. Our results demonstrate DHM as a reliable urine sediment analysis tool.