Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging.
Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging.
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内容感知帧插值（CAFI）：基于深度学习的时间超分辨率，用于快速生物成像。
DOI：
10.1038/s41592-023-02138-w
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发表时间：
2024
期刊：
影响因子：
48
通讯作者：
Laine,RomainF
中科院分区：
文献类型：
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作者：
Priessner,Martin;Gaboriau,DavidCA;Sheridan,Arlo;Lenn,Tchern;Garzon-Coral,Carlos;Dunn,AlexanderR;Chubb,JonathanR;Tousley,AidanM;Majzner,RobbieG;Manor,Uri;Vilar,Ramon;Laine,RomainF
高分辨率显微镜的发展使人们有可能在3D和随着时间的推移研究细胞过程。然而，由于光漂白和光毒性，观察快速的细胞动力学仍然具有挑战性。在这里，我们报告了两个内容感知帧插值（CAFI）深度学习网络的实现，Zooming SlowMo和Depth-Aware Video Frame Interpolation，它们非常适合准确预测图像对之间的图像，从而提高图像序列采集后的时间分辨率。我们表明，CAFI是能够理解的运动背景下的生物结构，可以执行比标准的插值方法。我们在从四种不同显微镜模式获得的12个不同数据集上对CAFI的性能进行了基准测试，并展示了其单颗粒跟踪和核分割的能力。CAFI潜在地允许减少样品上的光暴露和光毒性，以改善长期活细胞成像。模型以及训练和测试数据可通过ZeroCostDL 4 Mic平台获得。
The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching and phototoxicity. Here we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo and Depth-Aware Video Frame Interpolation, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series post-acquisition. We show that CAFI is capable of understanding the motion context of biological structures and can perform better than standard interpolation methods. We benchmark CAFI’s performance on 12 different datasets, obtained from four different microscopy modalities, and demonstrate its capabilities for single-particle tracking and nuclear segmentation. CAFI potentially allows for reduced light exposure and phototoxicity on the sample for improved long-term live-cell imaging. The models and the training and testing data are available via the ZeroCostDL4Mic platform.