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Verfasst von:Kumari, Pooja [VerfasserIn]   i
 Kern, Johann [VerfasserIn]   i
 Rädle, Matthias [VerfasserIn]   i
Titel:Self-supervised and zero-shot learning in multi-modal Raman light sheet microscopy
Verf.angabe:Pooja Kumari, Johann Kern and Matthias Raedle
E-Jahr:2024
Jahr:20 December 2024
Umfang:23 S.
Fussnoten:Dieser Artikel gehört zum Special issue: Sensors and advanced sensing techniques for computer vision applications ; Gesehen am 14.04.2025
Titel Quelle:Enthalten in: Sensors
Ort Quelle:Basel : MDPI, 2001
Jahr Quelle:2024
Band/Heft Quelle:24(2024), 24, Artikel-ID 8143, Seite 1-23
ISSN Quelle:1424-8220
Abstract:Advancements in Raman light sheet microscopy have provided a powerful, non-invasive, marker-free method for imaging complex 3D biological structures, such as cell cultures and spheroids. By combining 3D tomograms made by Rayleigh scattering, Raman scattering, and fluorescence detection, this modality captures complementary spatial and molecular data, critical for biomedical research, histology, and drug discovery. Despite its capabilities, Raman light sheet microscopy faces inherent limitations, including low signal intensity, high noise levels, and restricted spatial resolution, which impede the visualization of fine subcellular structures. Traditional enhancement techniques like Fourier transform filtering and spectral unmixing require extensive preprocessing and often introduce artifacts. More recently, deep learning techniques, which have shown great promise in enhancing image quality, face their own limitations. Specifically, conventional deep learning models require large quantities of high-quality, manually labeled training data for effective denoising and super-resolution tasks, which is challenging to obtain in multi-modal microscopy. In this study, we address these limitations by exploring advanced zero-shot and self-supervised learning approaches, such as ZS-DeconvNet, Noise2Noise, Noise2Void, Deep Image Prior (DIP), and Self2Self, which enhance image quality without the need for labeled and large datasets. This study offers a comparative evaluation of zero-shot and self-supervised learning methods, evaluating their effectiveness in denoising, resolution enhancement, and preserving biological structures in multi-modal Raman light sheet microscopic images. Our results demonstrate significant improvements in image clarity, offering a reliable solution for visualizing complex biological systems. These methods establish the way for future advancements in high-resolution imaging, with broad potential for enhancing biomedical research and discovery.
DOI:doi:10.3390/s24248143
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.3390/s24248143
 kostenfrei: Volltext: https://www.mdpi.com/1424-8220/24/24/8143
 DOI: https://doi.org/10.3390/s24248143
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:deep learning
 denoising
 fluorescence
 light sheet microscopy
 multi-mode
 Raman scattering
 Rayleigh scattering
 self-supervised learning
 spheroid
 super-resolution
 unsupervised learning
 zero-shot learning
K10plus-PPN:192283355X
Verknüpfungen:→ Zeitschrift

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