Kumari Pooja, Kern Johann, Raedle Matthias
CeMOS Research and Transfer Center, Mannheim University of Applied Sciences, 68163 Mannheim, Germany.
Universitätsklinikum Mannheim, Universität Heidelberg, 68167 Mannheim, Germany.
Sensors (Basel). 2024 Dec 20;24(24):8143. doi: 10.3390/s24248143.
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.
拉曼光片显微镜技术的进步为成像复杂的三维生物结构（如细胞培养物和球体）提供了一种强大的、非侵入性的、无标记方法。通过结合瑞利散射、拉曼散射和荧光检测生成的三维断层图像，这种成像方式能够获取互补的空间和分子数据，这对于生物医学研究、组织学和药物发现至关重要。尽管具有这些能力，但拉曼光片显微镜技术仍面临一些固有局限性，包括信号强度低、噪声水平高以及空间分辨率受限，这些都妨碍了对精细亚细胞结构的可视化。传统的增强技术，如傅里叶变换滤波和光谱解混，需要大量预处理，且常常会引入伪像。最近，深度学习技术在提高图像质量方面显示出巨大潜力，但也面临自身的局限性。具体而言，传统的深度学习模型需要大量高质量的、人工标注的训练数据来执行有效的去噪和超分辨率任务，而在多模态显微镜中获取这些数据具有挑战性。在本研究中，我们通过探索先进的零样本和自监督学习方法（如ZS-DeconvNet、Noise2Noise、Noise2Void、深度图像先验（DIP）和Self2Self）来解决这些局限性，这些方法无需标注和大型数据集即可提高图像质量。本研究对零样本和自监督学习方法进行了比较评估，评估了它们在多模态拉曼光片显微镜图像去噪、分辨率增强和保留生物结构方面的有效性。我们的结果表明图像清晰度有显著提高，为可视化复杂生物系统提供了可靠的解决方案。这些方法为高分辨率成像的未来发展奠定了基础，在增强生物医学研究和发现方面具有广泛的潜力。
