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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.
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Marlene Kallaß, Álvaro Barroso, Yussef Hanna, Steffi Ketelhut, Eva Döpker, Jürgen Schnekenburger, Björn Kemper, "Urine sediment analysis utilizing quantitative phase imaging with digital holographic microscopy," Proc. SPIE 12854, Label-free Biomedical Imaging and Sensing (LBIS) 2024, 128540G (12 March 2024); https://doi.org/10.1117/12.3002360