Deep learning 2D and 3D optical sectioning microscopy using cross-modality Pix2Pix cGAN image translation.
Deep learning 2D and 3D optical sectioning microscopy using cross-modality Pix2Pix cGAN image translation.
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DOI：
10.1364/boe.439894
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发表时间：
2021-11
影响因子：
3.4
通讯作者：
Huimin Zhuge;B. Summa;Jihun Hamm;J. Q. Brown
中科院分区：
文献类型：
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作者：
Huimin Zhuge;B. Summa;Jihun Hamm;J. Q. Brown
结构照明显微镜（SIM）从多个空间图案化的宽场图像重建样品的光学切片图像，但是传统的单个非图案化的宽场图像更便宜地获得，因为它们不需要生成专门的照明图案。在这项工作中，我们通过Pix2Pix条件生成对抗网络（cGAN）将宽视场荧光显微镜图像转换为光学切片的SIM图像。我们的模型显示了从宽场图像到光学切片的2D交叉模态图像转换的能力，并进一步展示了从宽场图像堆栈恢复3D光学切片体积的潜力。该模型的效用进行了测试，包括荧光珠和新鲜的人体组织样本的各种样品。
Structured illumination microscopy (SIM) reconstructs optically-sectioned images of a sample from multiple spatially-patterned wide-field images, but the traditional single non-patterned wide-field images are more inexpensively obtained since they do not require generation of specialized illumination patterns. In this work, we translated wide-field fluorescence microscopy images to optically-sectioned SIM images by a Pix2Pix conditional generative adversarial network (cGAN). Our model shows the capability of both 2D cross-modality image translation from wide-field images to optical sections, and further demonstrates potential to recover 3D optically-sectioned volumes from wide-field image stacks. The utility of the model was tested on a variety of samples including fluorescent beads and fresh human tissue samples.