Deep learning-based analysis of deep ultraviolet (UV) microscopy images for label-free, point-of-care hematological analysis

Nischita Kaza; Lyndie R. Chiou; Max H. Lenk; James J. M. Law; Kelly Mabry; Francisco E. Robles

doi:10.1117/12.3002126

27 March 2024 Deep learning-based analysis of deep ultraviolet (UV) microscopy images for label-free, point-of-care hematological analysis

Nischita Kaza, Lyndie R. Chiou, Max H. Lenk, James J. M. Law, Kelly Mabry, Francisco E. Robles

Author Affiliations +

Proceedings Volume 12850, Optical Diagnostics and Sensing XXIV: Toward Point-of-Care Diagnostics; 128500K (2024) https://doi.org/10.1117/12.3002126
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Hematological analysis is based on assessing changes in the numbers of different blood cells and their morphological, molecular, and cytogenetic properties via a complete blood count. It is integral to diagnose and monitor a range of blood conditions and diseases, ranging from allergies and infections to different types of cancers. The conventional approach to hematology analysis requires time-consuming protocols, multiple expensive chemical reagents, complex equipment, and highly trained personnel for operation, and presents a significant burden to patients and healthcare systems. There is a need for simple, fast, low-cost alternatives such as label-free techniques that eliminate the need for staining or exogenous labels. We recently demonstrated label-free hematology analysis using deep-ultraviolet (UV) microscopy, a high-resolution imaging technique that yields quantitative molecular and structural information from biological samples. In this work, we present a fast, automated analysis pipeline to classify and count the different blood cell types in single-channel UV microscopy images using a low-cost, compact deep-UV microscope. Our previous work focused primarily on white blood cells; here, we further add platelets and red blood cells. We train a YOLOv7-style network to identify and count different blood cells in smear images acquired from a deep-UV microscopy system. Our deep-UV microscope in an LED-based, compact, and portable configuration and single-step analysis pipeline could be further combined with UV-transparent PDMS-based microfluidic devices to develop a fully automated, low-cost, label-free hematology analyzer.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nischita Kaza, Lyndie R. Chiou, Max H. Lenk, James J. M. Law, Kelly Mabry, and Francisco E. Robles "Deep learning-based analysis of deep ultraviolet (UV) microscopy images for label-free, point-of-care hematological analysis", Proc. SPIE 12850, Optical Diagnostics and Sensing XXIV: Toward Point-of-Care Diagnostics, 128500K (27 March 2024); https://doi.org/10.1117/12.3002126

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