Fluorescence microscopy images usually show severe anisotropy in axial versus lateral resolution. This hampers downstream processing, i.e. the automatic extraction of quantitative biological data. While deconvolution methods and other techniques to address this problem exist, they are either time consuming to apply or limited in their ability to remove anisotropy. We propose a method to recover isotropic resolution from readily acquired anisotropic data. We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the network to. The network effectively learns to restore the full isotropic resolution by restoring the image under a trained, sample specific image prior. We apply our method to $3$ synthetic and $3$ real datasets and show that our results improve on results from deconvolution and state-of-the-art super-resolution techniques. Finally, we demonstrate that a standard 3D segmentation pipeline performs on the output of our network with comparable accuracy as on the full isotropic data.

本研究提出了一种使用卷积神经网络恢复各向同性分辨率的方法，能够针对荧光显微镜成像数据中子期/横向分辨率差异的问题提供有效的解决方案，并在3种合成数据和3种实际数据集上得到比清晰度恢复和超分辨率技术更好的成果，以及以标准3D分割流程的形式展示了该方法的实用性。

使用卷积神经网络对三维荧光显微图像进行各向同性重建