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Serial focussed ion beam scanning electron microscopy (FIB/SEM) enables imaging and assessment of subcellular structures on the mesoscale (10 nm to 10 µm). When applied to vitrified samples, serial FIB/SEM is also a means to target specific structures in cells and tissues while maintaining constituents’ hydration shells for in situ structural biology downstream. However, the application of serial FIB/SEM imaging of non-stained cryogenic biological samples is limited due to low contrast, curtaining, and charging artefacts. We address these challenges using a cryogenic plasma FIB/SEM. We evaluated the choice of plasma ion source and imaging regimes to produce high-quality SEM images of a range of different biological samples. Using an automated workflow we produced three-dimensional volumes of bacteria, human cells, and tissue, and calculated estimates for their resolution, typically achieving 20–50 nm. Additionally, a tag-free localisation tool for regions of interest is needed to drive the application of in situ structural biology towards tissue. The combination of serial FIB/SEM with plasma-based ion sources promises a framework for targeting specific features in bulk-frozen samples (>100 µm) to produce lamellae for cryogenic electron tomography.

As sample preparation and imaging techniques have expanded and improved to include a variety of options for larger sized and numbers of samples, the bottleneck in volumetric imaging is now data analysis. Annotation and segmentation are both common, yet difficult, data analysis tasks which are required to bring meaning to the volumetric data. The SuRVoS application has been updated and redesigned to provide access to both manual and machine learning-based segmentation and annotation techniques, including support for crowd sourced data. Combining adjacent, similar voxels (supervoxels) provides a mechanism for speeding up segmentation both in the painting of annotation and by training a segmentation model on a small amount of annotation. The support for layers allows multiple datasets to be viewed and annotated together which, for example, enables the use of correlative data (e.g. crowd-sourced annotations or secondary imaging techniques) to guide segmentation. The ability to work with larger data on high-performance servers with GPUs has been added through a client-server architecture and the Pytorch-based image processing and segmentation server is flexible and extensible, and allows the implementation of deep learning-based segmentation modules. The client side has been built around Napari allowing integration of SuRVoS into an ecosystem for open-source image analysis while the server side has been built with cloud computing and extensibility through plugins in mind. Together these improvements to SuRVoS provide a platform for accelerating the annotation and segmentation of volumetric and correlative imaging data across modalities and scales.