2020
DOI: 10.12688/f1000research.27158.1
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DeepCLEM: automated registration for correlative light and electron microscopy using deep learning

Abstract: In correlative light and electron microscopy (CLEM), the fluorescent images must be registered to the EM images with high precision. Due to the different contrast of EM and fluorescence images, automated correlation-based alignment is not directly possible, and registration is often done by hand using a fluorescent chromatin stain, or semi-automatically with fiducial markers. We introduce “DeepCLEM”, a fully automated CLEM registration workflow. A convolutional neural network predicts the fluorescent signal fr… Show more

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Cited by 9 publications
(5 citation statements)
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“…We foresee that the multimodal datasets obtained using this method will be instrumental in forthcoming machine learning applications ( Eckstein et al, 2020 ; Liu et al, 2020 ; Heinrich et al, 2021 ). Thus far, applications of registered EM-FM datasets appear to be limited to facilitating registration of sequential CLEM data using artificial predictions for the fluorescence signal ( Ounkomol et al, 2018 ; Seifert et al, 2020 ). Volume EM datasets, particularly in connectomics, are now routinely segmented via deep convolutional neural networks ( Buhmann et al, 2021 ; Heinrich et al, 2021 ).…”
Section: Discussionmentioning
confidence: 99%
“…We foresee that the multimodal datasets obtained using this method will be instrumental in forthcoming machine learning applications ( Eckstein et al, 2020 ; Liu et al, 2020 ; Heinrich et al, 2021 ). Thus far, applications of registered EM-FM datasets appear to be limited to facilitating registration of sequential CLEM data using artificial predictions for the fluorescence signal ( Ounkomol et al, 2018 ; Seifert et al, 2020 ). Volume EM datasets, particularly in connectomics, are now routinely segmented via deep convolutional neural networks ( Buhmann et al, 2021 ; Heinrich et al, 2021 ).…”
Section: Discussionmentioning
confidence: 99%
“…CryoCLEM approaches (Lucić et al, 2007;Sartori et al, 2007;Plitzko et al, 2009;Hampton et al, 2017), including those involving cryoVEM methods (Vidavsky et al, 2016) such as cryoSBF-SEM (Hoffman et al, 2020) and cryoFIB-SEM (Gorelick et al, 2019), are not yet routine but constitute an actively developing field given their capacity to highlight molecular identities during ultrastructural examination of cells and tissues without chemical fixation and staining artifacts (Bharat and Kukulski, 2019). Increasing efforts to automate these techniques will help to make them routine for many specimens in the near future (Klumpe et al, 2021;Yang et al, 2021;Weiner et al, 2022), including bacteria and other pathogens (Liedtke et al, 2022), particularly as ML strategies are incorporated into many steps of the workflows (Seifert et al, 2020). Recent implementations have demonstrated cryoCLEM of entire cells using cryoSBF-SEM followed by cryoFIB-SEM lamellae milling and cryoET of targeted areas of interest (Wu et al, 2020), as well as for tissues using the 'lift-out' technique (Schaffer et al, 2019;Kuba et al, 2021) and entire organisms using 'serial lift-out' (Schiøtz et al, 2023).…”
Section: Correlative Light and Electron Microscopy For Targeted High-...mentioning
confidence: 99%
“…Finally, while the finer registration is automated, the coarse registration of finding the corresponding orthoslice within the 3D tomography data is manually intensive and tedious. The coarse registration can also be optimised through artificial intelligence, allowing the whole Deep-XFCT procedure to be streamlined through automation [45].…”
Section: Segmentation Enhancement Pathwaysmentioning
confidence: 99%