Detection and classification of urine components utilizing quantitative phase imaging and machine learning

Marlene Kallaß; Yussef Hanna; Álvaro Barroso; Steffi Ketelhut; Jürgen Schnekenburger; Björn Kemper

doi:10.1117/12.2675553

9 August 2023 Detection and classification of urine components utilizing quantitative phase imaging and machine learning

Marlene Kallaß, Yussef Hanna, Álvaro Barroso, Steffi Ketelhut, Jürgen Schnekenburger, Björn Kemper

Author Affiliations +

Proceedings Volume 12630, Advances in Microscopic Imaging IV; 126300I (2023) https://doi.org/10.1117/12.2675553
Event: European Conferences on Biomedical Optics, 2023, Munich, Germany

Abstract

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.

Citation Download Citation

Marlene Kallaß, Yussef Hanna, Álvaro Barroso, Steffi Ketelhut, Jürgen Schnekenburger, and Björn Kemper "Detection and classification of urine components utilizing quantitative phase imaging and machine learning", Proc. SPIE 12630, Advances in Microscopic Imaging IV, 126300I (9 August 2023); https://doi.org/10.1117/12.2675553

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