Model and learning-based computational 3D phase microscopy with intensity diffraction tomography
Model and learning-based computational 3D phase microscopy with intensity diffraction tomography
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DOI：
10.23919/eusipco47968.2020.9287407
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Alex Matlock;Yujia Xue;Yunzhe Li;Shiyi Cheng;Waleed Tahir;L. Tian
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作者：
Alex Matlock;Yujia Xue;Yunzhe Li;Shiyi Cheng;Waleed Tahir;L. Tian
强度衍射断层扫描（IDT）是一种新的计算显微镜技术，可提供生物样品的定量，体积，大型视野（FOV）相成像。该方法使用计算有效的反向散射模型来从单个焦平面下在各种照明下进行的强度测量中回收弱散射对象的3D相容量。 IDT很容易在配备LED阵列源的标准显微镜中实现，并且不需要外源对比度剂，这使得该技术可以广泛用于生物学研究。我们提出了两个基于模型的计算照明策略，即多路复用IDT（MIDT）[1]和Annular IDT（AIDT）[2]，它们分别在硬件有限的4Hz和10Hz的量速率下分别实现了高通量定量3D对象相恢复。我们说明了有关活上皮颊细胞和秀丽隐杆线虫蠕虫的这些技术。对于使用IDT的强散射对象恢复，我们提出了一个不确定性量化框架，用于评估基于深度学习的相位恢复方法的可靠性[3]。该框架提供了对神经网络预测置信度水平的每个像素评估，从而可以有效且可靠的复杂对象恢复。这种不确定性学习框架广泛适用于可靠的基于深度学习的生物医学成像技术，并显示出IDT的巨大潜力。
Intensity Diffraction Tomography (IDT) is a new computational microscopy technique providing quantitative, volumetric, large field-of-view (FOV) phase imaging of biological samples. This approach uses computationally efficient inverse scattering models to recover 3D phase volumes of weakly scattering objects from intensity measurements taken under diverse illumination at a single focal plane. IDT is easily implemented in a standard microscope equipped with an LED array source and requires no exogenous contrast agents, making the technology widely accessible for biological research.Here, we discuss model and learning-based approaches for complex 3D object recovery with IDT. We present two model-based computational illumination strategies, multiplexed IDT (mIDT) [1] and annular IDT (aIDT) [2], that achieve high-throughput quantitative 3D object phase recovery at hardware-limited 4Hz and 10Hz volume rates, respectively. We illustrate these techniques on living epithelial buccal cells and Caenorhabditis elegans worms. For strong scattering object recovery with IDT, we present an uncertainty quantification framework for assessing the reliability of deep learning-based phase recovery methods [3]. This framework provides per-pixel evaluation of a neural network predictions confidence level, allowing for efficient and reliable complex object recovery. This uncertainty learning framework is widely applicable for reliable deep learning-based biomedical imaging techniques and shows significant potential for IDT.