HoloHema: Digital Holographic Hematology Analyzer
Andreas Erik Gejl Madsen

TL;DR
This paper presents the development and validation of digital holographic microscopy prototypes employing CNNs for white blood cell classification in point-of-care devices, demonstrating high accuracy and potential for sepsis biomarker detection.
Contribution
Introduces novel lensless DHM prototypes with CNN-based classification, achieving high accuracy and demonstrating potential for sepsis biomarker measurement in PoC settings.
Findings
Achieved up to 92.65% classification accuracy with lensless DHM
Demonstrated derivation of sepsis biomarker MDW using DHM
Explored ML-based holographic reconstruction techniques
Abstract
This industrial Ph.D. project, carried out in collaboration between Radiometer Medical ApS and SDU Centre for Photonics Engineering at the University of Southern Denmark, explored the use of digital holographic microscopy (DHM) for the purposes of differential white blood cell counts (dWBCs) in point-of-care (PoC) devices for acute care settings. Two DHM prototypes were developed; an initial lens-based system serving as the foundation for algorithm development, and experimental validation of the approach, achieving 89.6% classification accuracy on a 3-part differential, and a subsequent lensless system for simplified design and increased field-of-view (FoV). Both prototypes employed convolutional neural networks (CNNs) for cell classification. With further optimizations, the lensless system achieved classification accuracies of 92.65% and 89.44% on the 3-part and 5-part differential,…
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Taxonomy
TopicsDigital Holography and Microscopy · Microfluidic and Bio-sensing Technologies · Cell Image Analysis Techniques
