Coordinate-based neural representations for computational adaptive optics in widefield microscopy

IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Nature Machine Intelligence Pub Date : 2024-06-24 DOI:10.1038/s42256-024-00853-3
Iksung Kang, Qinrong Zhang, Stella X. Yu, Na Ji
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Abstract

Widefield microscopy is widely used for non-invasive imaging of biological structures at subcellular resolution. When applied to a complex specimen, its image quality is degraded by sample-induced optical aberration. Adaptive optics can correct wavefront distortion and restore diffraction-limited resolution but require wavefront sensing and corrective devices, increasing system complexity and cost. Here we describe a self-supervised machine learning algorithm, CoCoA, that performs joint wavefront estimation and three-dimensional structural information extraction from a single-input three-dimensional image stack without the need for external training datasets. We implemented CoCoA for widefield imaging of mouse brain tissues and validated its performance with direct-wavefront-sensing-based adaptive optics. Importantly, we systematically explored and quantitatively characterized the limiting factors of CoCoA’s performance. Using CoCoA, we demonstrated in vivo widefield mouse brain imaging using machine learning-based adaptive optics. Incorporating coordinate-based neural representations and a forward physics model, the self-supervised scheme of CoCoA should be applicable to microscopy modalities in general. Adaptive optics (AO) corrects aberrations and restores resolution but requires specialized hardware. Kang et al. introduce a self-supervised AO method (CoCoA) for widefield microscopy, achieving in vivo mouse brain imaging without wavefront sensors.

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用于宽视场显微镜中计算自适应光学的基于坐标的神经表征
宽场显微镜被广泛用于对亚细胞分辨率的生物结构进行无创成像。当应用于复杂样本时,其图像质量会因样本引起的光学像差而下降。自适应光学技术可以校正波前畸变,恢复衍射极限分辨率,但需要波前传感和校正设备,从而增加了系统的复杂性和成本。在这里,我们介绍了一种自监督机器学习算法 CoCoA,它可以从单一输入的三维图像堆栈中执行联合波前估计和三维结构信息提取,而无需外部训练数据集。我们将 CoCoA 应用于小鼠脑组织的宽场成像,并用基于直接波前传感的自适应光学验证了它的性能。重要的是,我们系统地探索并定量描述了限制 CoCoA 性能的因素。通过使用 CoCoA,我们利用基于机器学习的自适应光学技术演示了活体宽视场小鼠大脑成像。结合基于坐标的神经表征和前向物理模型,CoCoA 的自监督方案应适用于一般的显微镜模式。
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来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
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