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.
We explored the capabilities of quantitative phase imaging (QPI) with digital holographic microscopy (DHM) in combination with machine learning (ML) approaches for the characterization and classification of urine sediments. Bright-field images and off-axis holograms of a liquid control for urine analysis were acquired with a modular DHM system. From the retrieved images, particle morphology parameters were extracted by segmentation procedures. In addition, the ability of supervised ML-algorithms to classify and identify urine sediment components based on biophysical parameters was evaluated. The results demonstrate DHM in combination with ML as a prospective tool for urine analysis.
For increased efficiency and standardization, automated urine screening is highly desirable. We explored the capabilities of quantitative phase imaging (QPI) by digital holographic microscopy (DHM) for the characterization and classification of urine sediments based on biophysical parameter sets. Digital off-axis holograms from a liquid control for urine analysis were acquired with a modular DHM system attached to a commercial optical microscope. From the retrieved quantitative phase images, various particle morphology parameters were extracted to differentiate and quantify particles contained in the test samples. Our results show that DHM represents a robust and promising tool for t label-free characterization of urine sediments with enhanced throughput.
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