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Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning

机译:使用深度学习的地面真理缺陷的基于真理的缺陷的纳米检查图像中的人工制品

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摘要

Image denoising or artefact removal using deep learning is possible in the availability of supervised training dataset acquired in real experiments or synthesized using known noise models. Neither of the conditions can be fulfilled for nanoscopy (super-resolution optical microscopy) images that are generated from microscopy videos through statistical analysis techniques. Due to several physical constraints, a supervised dataset cannot be measured. Further, the non-linear spatio-temporal mixing of data and valuable statistics of fluctuations from fluorescent molecules that compete with noise statistics. Therefore, noise or artefact models in nanoscopy images cannot be explicitly learned. Here, we propose a robust and versatile simulation-supervised training approach of deep learning auto-encoder architectures for the highly challenging nanoscopy images of sub-cellular structures inside biological samples. We show the proof of concept for one nanoscopy method and investigate the scope of generalizability across structures, and nanoscopy algorithms not included during simulation-supervised training. We also investigate a variety of loss functions and learning models and discuss the limitation of existing performance metrics for nanoscopy images. We generate valuable insights for this highly challenging and unsolved problem in nanoscopy, and set the foundation for the application of deep learning problems in nanoscopy for life sciences.
机译:在实际实验中获取的监督训练数据集或使用已知噪声模型合成的监督训练数据集中,可以使用深度学习的图像去噪或人工制品去除。通过通过统计分析技术从显微镜视频产生的纳米镜(超分辨率光学显微镜)图像都没有满足条件。由于几个物理约束,无法测量监督数据集。此外,与噪声统计竞争的荧光分子的数据和有价值的荧光分子的非线性时空混合和荧光分子的波动统计。因此,纳米镜图像中的噪声或人工制品不能明确学习。在这里,我们提出了一种强大而多功能的模拟监督深度学习自动编码器架构的培训方法,用于生物样本内的亚细胞结构的高度挑战性纳米镜图像。我们展示了一种纳米镜检查方法的概念证明,并研究了结构上的概括性范围,以及在模拟监督培训期间不包括的纳米检查算法。我们还调查各种损失函数和学习模型,并讨论纳米镜图像现有性能度量的限制。我们为纳米镜检查具有高度挑战和未解决的问题的宝贵见解,并为生命科学进行了纳米镜检查的深度学习问题的应用基础。

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