Enhancing Fluorescence Image Analysis through Deep Learning

Fluorescence imaging plays a crucial role in studying biological processes and materials across various industrial applications. However, the manual analysis of fluorescence images is time-consuming and prone to errors. To address these challenges, we propose novel machine learning-based approaches to enhance the VIDAS® device - an automated immunoassay system employed for medical condition detection. The current setup utilizes a photodiode for luminescence capture, which exhibits limitations when applied to spatially distributed signals. To overcome this limitation, we explore the use of a CMOS sensor to capture two-dimensional images of cuvettes, enabling a more comprehensive analysis of the system. Our proposed solution involves generating reconstructed images that rectify potential defects, leading to improved and unbiased fluorescence estimation. Through extensive experimentation, we demonstrate that employing the reconstructed images enables more accurate measurements, particularly in the presence of defects. Our methodology encompasses deep learning and semantic segmen-tation techniques, allowing robust fluorescence image analysis.