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40 items found (0 items not approved), displaying 1 to 10.[First/Prev] 1, 2, 3, 4 [Next/Last]

Instruments / Technical Area	Publication Details	Published
	Cryo-plasma FIB/SEM volume imaging of biological specimens Maud Dumoux , Thomas Glen , Jake L. R. Smith , Elaine M. L. Ho , Luis Ma Perdigão , Avery Pennington , Sven Klumpe , Neville B. Y. Yee , David A. Farmer , Pui Y. A. Lai , William Bowles , Ron Kelley , Jürgen M Plitzko , Liang Wu , Mark Basham , Daniel K. Clare , C. Alistair Siebert , Michele C. Darrow , James H. Naismith , Michael Grange Elife 12 DOI: 10.7554/eLife.83623 Open Access Show Abstract ▼ Abstract: Serial focussed ion beam scanning electron microscopy (FIB/SEM) enables imaging and assessment of sub-cellular 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 (cryo-pFIB/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 to 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.	Feb 2023
Insertion Devices	Using artificial immune systems to sort and shim insertion devices at Diamond Light Source Joss Whittle , Mark Basham , Zena Patel , Edward Rial , Robert Oates , Yousef Moazzam Journal Of Physics: Conference Series 2380 14th International Conference on Synchrotron Radiation Instrumentation (SRI 2021) DOI: 10.1088/1742-6596/2380/1/012022 Open Access Show Abstract ▼ Abstract: This work presents the Opt-ID software developed by the Rosalind Franklin Institute (RFI) and Diamond Light Source (DLS) in collaboration with Helmholtz-Zentrum Berlin (HZB). Opt-ID allows for efficient simulation of synchrotron Insertion Devices (ID) and the B fields produced by a given arrangement of candidate magnets. It provides an optimization framework built on the Artificial Immune System (AIS) algorithm for swapping and adjusting magnets within an ID to observe how these changes would affect the magnetic field of a real-world device, guiding ID builders in the steps they should take during ID tuning. Code for Opt-ID is provided open-source under the Apache-2.0 License on Github: https://github.com/rosalindfranklininstitute/Opt-ID	Dec 2022
I13-2-Diamond Manchester Imaging	Comparison of methods to segment variable-contrast XCT images of methane-bearing sand using U-nets trained on single dataset sub-volumes Fernando Alvarez-Borges , Oliver N. F. King , Bangalore N. Madhusudhan , Thomas Connolley , Mark Basham , Sharif I. Ahmed Methane 2, 1 - 23 DOI: 10.3390/methane2010001 Diamond Proposal Number(s): [16205] Open Access Show Abstract ▼ Abstract: Methane (CH4) hydrate dissociation and CH4 release are potential geohazards currently investigated using X-ray computed tomography (XCT). Image segmentation is an important data processing step for this type of research. However, it is often time consuming, computing resource-intensive, operator-dependent, and tailored for each XCT dataset due to differences in greyscale contrast. In this paper, an investigation is carried out using U-Nets, a class of Convolutional Neural Network, to segment synchrotron XCT images of CH4-bearing sand during hydrate formation, and extract porosity and CH4 gas saturation. Three U-Net deployments previously untried for this task are assessed: (1) a bespoke 3D hierarchical method, (2) a 2D multi-label, multi-axis method and (3) RootPainter, a 2D U-Net application with interactive corrections. U-Nets are trained using small, targeted hand-annotated datasets to reduce operator time. It was found that the segmentation accuracy of all three methods surpass mainstream watershed and thresholding techniques. Accuracy slightly reduces in low-contrast data, which affects volume fraction measurements, but errors are small compared with gravimetric methods. Moreover, U-Net models trained on low-contrast images can be used to segment higher-contrast datasets, without further training. This demonstrates model portability, which can expedite the segmentation of large datasets over short timespans.	Dec 2022
	Volume segmantics: a Python package for semantic segmentation of volumetric data using pre-trained PyTorch deep learning models Oliver N. F. King , Dimitrios Bellos , Mark Basham Journal Of Open Source Software 7 DOI: 10.21105/joss.04691 Open Access Show Abstract ▼ Abstract: Segmentation of 3-dimensional (3D, volumetric) images is a widely used technique that allows interpretation and quantification of experimental data collected using a number of techniques (for example, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Electron Tomography (ET)). Although the idea of semantic segmentation is a relatively simple one, giving each pixel a label that defines what it represents (e.g cranial bone versus brain tissue); due to the subjective and laborious nature of the manual labelling task coupled with the huge size of the data (multi-GB files containing billions of pixels) this process is often a bottleneck in imaging workflows. In recent years, deep learning has brought models capable of fast and accurate interpretation of image data into the toolbox available to scientists. These models are often trained on large image datasets that have been annotated at great expense. In many cases however, scientists working on novel samples and using new imaging techniques do not yet have access to large stores of annotated data. To overcome this issue, simple software tools that allow the scientific community to create segmentation models using relatively small amounts of training data are required. Volume Segmantics is a Python package that provides a command line interface (CLI) as well as an Application Programming Interface (API) for training 2-dimensional (2D) PyTorch (Paszke et al., 2019) deep learning models on small amounts of annotated 3D image data. The package also enables applying these models to new (often much larger) 3D datasets to speed up the process of semantic segmentation.	Oct 2022
	Computing for optimized biological microscopy data processing and analysis at the Rosalind Franklin Institute Luís M. A. Perdigão , Neville B.-Y Yee , Elaine M. L. Ho , Avery H. Pennington , Oliver N. F. King , Michele C. Darrow , Mark Basham Microscopy And Microanalysis 28, 1462 - 1464 Microscopy & Microanalysis 2022: B07 - 3D Volume Electron Microscopy in Biology Research DOI: 10.1017/S1431927622005931 Show Abstract ▼ Abstract: Recent developments in experimental microscopy techniques have led to improvements in the way we visualize various biological phenomena. Presently, state-of-art microscopy involves cryogenic sample preparation, 3D correlative microscopy and milling, followed by tilt series acquisition of biological volumes leading to datasets with nanometer scale information. While this workflow is technically possible, it is still challenging to collect, process, and analyze these large datasets, especially when the workflow includes correlative imaging and segmentation steps. In the Artificial Intelligence and Informatics group (AI&I) at The Rosalind Franklin Institute we are automating these workflow steps to solve computationally difficult and time-intensives problems by developing open-source software tools. Here, we present some notable examples.	Aug 2022
I23-Long wavelength MX	High performance Savu software for fast 3D model-based iterative reconstruction of large data at Diamond Light Source Daniil Kazantsev , Nicola Wadeson , Mark Basham Softwarex 19 DOI: 10.1016/j.softx.2022.101157 Open Access Show Abstract ▼ Abstract: The challenge of processing big data effectively and efficiently is crucial for many synchrotron facilities which can collect up to several petabytes of data annually. At Diamond Light Source, the tomographic data is reconstructed with Python-based software Savu which utilises Message Passing Interface protocols to efficiently reconstruct parallel beam geometry data. When projection data is undersampled and/or noisy, regularised iterative reconstruction methods can provide a better reconstruction quality than direct methods. The iterative methods, however, require significantly more computational resources than direct methods and their usability is impeded by the choice of additional hyper-parameters. Notably, the use of 2D regularised iterative methods for reconstruction of 3D objects results in inconsistent (saw-shaped) features in a perpendicular to slicing orientation. Due to large data sizes, fully 3D regularised model-based iterative reconstruction is problematic or impossible in practice due to high memory requirements and long processing times. In this work, we demonstrate a practical solution which enables an approximated full 3D regularised iterative reconstruction running in parallel on a computing cluster. This modification delivers an equivalent to exact 3D reconstruction quality of data volumes with a high computational efficiency.	Jul 2022
	Diamond: The Game – a board game for secondary school students promoting scientific careers and experiences Claire Murray , Matthew Dunstan , Catherine Heron , Laura Holland , Sophy Palmer , David Price , Mark Basham Research For All Research For All DOI: 10.14324/RFA.06.1.14 Open Access Show Abstract ▼ Abstract: Diamond: The Game is a board game designed for secondary school students (aged 11–18) to enable them to explore a broad variety of science, technology, engineering and mathematics (STEM) careers, STEM subjects and life as a scientist. Board games are a reusable and entertaining way to directly engage students in STEM, but careful consideration of mechanics, messages and accessibility is required to successfully deliver on this goal. Diamond: The Game was designed and evaluated against these considerations. The inclusive approach to design resulted in a better and more accessible game for all. Its success is further evident in the rise in the number of players who would consider a career as a scientist or an engineer after playing. The opportunities to explore collaboration, failure and the interdisciplinary nature of science in the game were particularly highlighted in discussions with students, teachers and careers advisers.	Jun 2022
B24-Cryo Soft X-ray Tomography I13-2-Diamond Manchester Imaging Krios I-Titan Krios I at Diamond	SuRVoS 2: accelerating annotation and segmentation for large volumetric bioimage workflows across modalities and scales Avery Pennington , Oliver N. F. King , Win Min Tun , Elaine M. L. Ho , Imanol Luengo , Michele C. Darrow , Mark Basham Frontiers In Cell And Developmental Biology 10 DOI: 10.3389/fcell.2022.842342 Open Access Show Abstract ▼ Abstract: 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.	Apr 2022
I13-2-Diamond Manchester Imaging	Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models Dimitrios Bellos , Mark Basham , Tony Pridmore , Andrew P. French Scientific Reports 11 DOI: 10.1038/s41598-021-02466-x Diamond Proposal Number(s): [9396] Open Access Show Abstract ▼ Abstract: Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much more strenuous than in 2D images. For 4D time-series tomograms, this is usually handled by segmenting the constituent tomograms independently through time with 3D convolutional neural networks. Inter-volume information is therefore not utilized, potentially leading to temporal incoherence. In this paper, we attempt to resolve this by proposing two hidden Markov model variants that refine 4D segmentation labels made by 3D convolutional neural networks working on each time point. Our models utilize not only inter-volume information, but also the prediction confidence generated by the 3D segmentation convolutional neural networks themselves. To the best of our knowledge, this is the first attempt to refine 4D segmentations made by 3D convolutional neural networks using hidden Markov models. During experiments we test our models, qualitatively, quantitatively and behaviourally, using prespecified segmentations. We demonstrate in the domain of time series tomograms which are typically undersampled to allow more frequent capture; a particularly challenging problem. Finally, our dataset and code is publicly available.	Dec 2021
DIAD-Dual Imaging and Diffraction Beamline	Beamline K11 DIAD: a new instrument for dual imaging and diffraction at Diamond Light Source Christina Reinhard , Michael Drakopoulos , Sharif I. Ahmed , Hans Deyhle , Andrew James , Christopher M. Charlesworth , Martin Burt , John Sutter , Steven Alexander , Peter Garland , Thomas Yates , Russell Marshall , Ben Kemp , Edmund Warrick , Armando Pueyos , Ben Bradnick , Maurizio Nagni , A. Douglas Winter , Jacob Filik , Mark Basham , Nicola Wadeson , Oliver N. F. King , Navid Aslani , Andrew J. Dent Journal Of Synchrotron Radiation 28 DOI: 10.1107/S1600577521009875 Open Access Show Abstract ▼ Abstract: The Dual Imaging and Diffraction (DIAD) beamline at Diamond Light Source is a new dual-beam instrument for full-field imaging/tomography and powder diffraction. This instrument provides the user community with the capability to dynamically image 2D and 3D complex structures and perform phase identification and/or strain mapping using micro-diffraction. The aim is to enable in situ and in operando experiments that require spatially correlated results from both techniques, by providing measurements from the same specimen location quasi-simultaneously. Using an unusual optical layout, DIAD has two independent beams originating from one source that operate in the medium energy range (7–38 keV) and are combined at one sample position. Here, either radiography or tomography can be performed using monochromatic or pink beam, with a 1.4 mm × 1.2 mm field of view and a feature resolution of 1.2 µm. Micro-diffraction is possible with a variable beam size between 13 µm × 4 µm and 50 µm × 50 µm. One key functionality of the beamline is image-guided diffraction, a setup in which the micro-diffraction beam can be scanned over the complete area of the imaging field-of-view. This moving beam setup enables the collection of location-specific information about the phase composition and/or strains at any given position within the image/tomography field of view. The dual beam design allows fast switching between imaging and diffraction mode without the need of complicated and time-consuming mode switches. Real-time selection of areas of interest for diffraction measurements as well as the simultaneous collection of both imaging and diffraction data of (irreversible) in situ and in operando experiments are possible.	Nov 2021

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