Mr. Max H. Lenk Profile

Max H. Lenk

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Proceedings Article | 27 March 2024 Poster + Paper

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

Nischita Kaza, Lyndie Chiou, Max Lenk, James J. Law, Kelly Mabry, Francisco Robles

Proceedings Volume 12850, 128500K (2024) https://doi.org/10.1117/12.3002126

KEYWORDS: Microscopy, Deep ultraviolet, Blood, White blood cells, Red blood cells, Image segmentation, Image classification, Chemical analysis

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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.

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