Deep learning enables stochastic optical reconstruction microscopy-like superresolution image reconstruction from conventional microscopy.
Deep learning enables stochastic optical reconstruction microscopy-like superresolution image reconstruction from conventional microscopy.
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DOI：
10.1016/j.isci.2023.108145
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发表时间：
2023-11-17
期刊：
影响因子：
5.8
通讯作者：
Jiang, Wei
中科院分区：
文献类型：
--
作者：
Xu, Lei;Kan, Shichao;Yu, Xiying;Liu, Ye;Fu, Yuxia;Peng, Yiqiang;Liang, Yanhui;Cen, Yigang;Zhu, Changjun;Jiang, Wei
尽管深度学习在转换低分辨率图像方面具有巨大潜力，但在从宽场（传统）显微镜实现高质量超分辨率显微成像方面仍面临重大挑战。在此，我们介绍X显微镜技术，这是一种由两个深度学习子网UR - Net - 8和X - Net组成的计算工具，它能够从宽场图像中实现类似随机光学重建显微镜（STORM）的超分辨率显微图像重建，且输入尺寸灵活。X显微镜技术使用各种亚细胞结构的样本进行训练，包括细胞骨架丝、点状、蜂巢状和纳米簇状结构，以生成能够产生与类似STORM图像质量相当的预测模型。除了能够进行多色超分辨率图像重建外，X显微镜技术还便于从不同的传统显微系统进行超分辨率图像重建。X显微镜技术的这些能力为使超分辨率显微镜技术惠及更广泛的用户带来了广阔前景，突破了设备精良实验室的限制。
- X显微镜技术可从低分辨率宽场图像实现超分辨率显微镜图像重建
- X显微镜技术由两个基于深度学习的子网组成
- 经过训练的X显微镜技术可从各种生物结构的宽场图像预测超分辨率显微镜图像
- X显微镜技术有助于多色和跨模态超分辨率显微镜图像重建
医学成像；光学成像；机器学习
Despite its remarkable potential for transforming low-resolution images, deep learning faces significant challenges in achieving high-quality superresolution microscopy imaging from wide-field (conventional) microscopy. Here, we present X-Microscopy, a computational tool comprising two deep learning subnets, UR-Net-8 and X-Net, which enables STORM-like superresolution microscopy image reconstruction from wide-field images with input-size flexibility. X-Microscopy was trained using samples of various subcellular structures, including cytoskeletal filaments, dot-like, beehive-like, and nanocluster-like structures, to generate prediction models capable of producing images of comparable quality to STORM-like images. In addition to enabling multicolour superresolution image reconstructions, X-Microscopy also facilitates superresolution image reconstruction from different conventional microscopic systems. The capabilities of X-Microscopy offer promising prospects for making superresolution microscopy accessible to a broader range of users, going beyond the confines of well-equipped laboratories. X-Microscopy enables SRM reconstructions from low-resolution WF images X-Microscopy comprises two deep learning-based subnetworks The trained X-Microscopy predicts SRMs from WFs of various biological structures X-Microscopy facilitates multicolor and cross-modality SRM reconstructions Medical imaging; Optical imaging; Machine learning
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影响因子：
48
作者：
Fang L;Monroe F;Novak SW;Kirk L;Schiavon CR;Yu SB;Zhang T;Wu M;Kastner K;Latif AA;Lin Z;Shaw A;Kubota Y;Mendenhall J;Zhang Z;Pekkurnaz G;Harris K;Howard J;Manor U
通讯作者：
Manor U
影响因子：
16.6
作者：
Jin, Luhong;Liu, Bei;Hahn, Klaus M.
通讯作者：
Hahn, Klaus M.
影响因子：
5.8
作者：
Arganda-Carreras, Ignacio;Kaynig, Verena;Seung, H. Sebastian
通讯作者：
Seung, H. Sebastian
影响因子：
16.6
作者：
Eulenberg P;Köhler N;Blasi T;Filby A;Carpenter AE;Rees P;Theis FJ;Wolf FA
通讯作者：
Wolf FA
影响因子：
5.3
作者：
Fuhrmann, Martin;Gockel, Nala;Willig, Katrin I.
通讯作者：
Willig, Katrin I.