Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks

Fluorescence microscopy images usually show severe anisotropy in axial versus lateral resolution. This hampers downstream processing, i.e. the automatic extraction of quantitative biological data. While deconvolution methods and other techniques to address this problem exist, they are either time consuming to apply or limited in their ability to remove anisotropy. We propose a method to recover isotropic resolution from readily acquired anisotropic data. We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the network to. The network effectively learns to restore the full isotropic resolution by restoring the image under a trained, sample specific image prior. We apply our method to $3$ synthetic and $3$ real datasets and show that our results improve on results from deconvolution and state-of-the-art super-resolution techniques. Finally, we demonstrate that a standard 3D segmentation pipeline performs on the output of our network with comparable accuracy as on the full isotropic data

Damage function for historic paper. Part I: Fitness for use

Background In heritage science literature and in preventive conservation practice, damage functions are used to model material behaviour and specifically damage (unacceptable change), as a result of the presence of a stressor over time. For such functions to be of use in the context of collection management, it is important to define a range of parameters, such as who the stakeholders are (e.g. the public, curators, researchers), the mode of use (e.g. display, storage, manual handling), the long-term planning horizon (i.e. when in the future it is deemed acceptable for an item to become damaged or unfit for use), and what the threshold of damage is, i.e. extent of physical change assessed as damage. Results In this paper, we explore the threshold of fitness for use for archival and library paper documents used for display or reading in the context of access in reading rooms by the general public. Change is considered in the context of discolouration and mechanical deterioration such as tears and missing pieces: forms of physical deterioration that accumulate with time in libraries and archives. We also explore whether the threshold fitness for use is defined differently for objects perceived to be of different value, and for different modes of use. The data were collected in a series of fitness-for-use workshops carried out with readers/visitors in heritage institutions using principles of Design of Experiments. Conclusions The results show that when no particular value is pre-assigned to an archival or library document, missing pieces influenced readers/visitors’ subjective judgements of fitness-for-use to a greater extent than did discolouration and tears (which had little or no influence). This finding was most apparent in the display context in comparison to the reading room context. The finding also best applied when readers/visitors were not given a value scenario (in comparison to when they were asked to think about the document having personal or historic value). It can be estimated that, in general, items become unfit when text is evidently missing. However, if the visitor/reader is prompted to think of a document in terms of its historic value, then change in a document has little impact on fitness for use

Improving Blind Spot Denoising for Microscopy

Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervised methods have emerged. Such methods are trained directly on the images that are to be denoised and do not require additional paired training data. While achieving remarkable results, self-supervised methods can produce high-frequency artifacts and achieve inferior results compared to supervised approaches. Here we present a novel way to improve the quality of self-supervised denoising. Considering that light microscopy images are usually diffraction-limited, we propose to include this knowledge in the denoising process. We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network. As a consequence, we are able to eliminate high-frequency artifacts and achieve self-supervised results that are very close to the ones achieved with traditional supervised methods.Comment: 15 pages, 4 figure

Efficient Bayesian-based Multi-View Deconvolution

Light sheet fluorescence microscopy is able to image large specimen with high resolution by imaging the sam- ples from multiple angles. Multi-view deconvolution can significantly improve the resolution and contrast of the images, but its application has been limited due to the large size of the datasets. Here we present a Bayesian- based derivation of multi-view deconvolution that drastically improves the convergence time and provide a fast implementation utilizing graphics hardware.Comment: 48 pages, 20 figures, 1 table, under review at Nature Method

Nonlinear optics: Nonlinear virtues of multimode fibre

Supercontinuum generation — the extreme spectral broadening of laser light (a span from the ultraviolet to the mid-infrared is possible) — is a fascinating process that takes place in a dispersive and strongly nonlinear optical medium

Quantum fluctuations can promote or inhibit glass formation

The very nature of glass is somewhat mysterious: while relaxation times in glasses are of sufficient magnitude that large-scale motion on the atomic level is essentially as slow as it is in the crystalline state, the structure of glass appears barely different than that of the liquid that produced it. Quantum mechanical systems ranging from electron liquids to superfluid helium appear to form glasses, but as yet no unifying framework exists connecting classical and quantum regimes of vitrification. Here we develop new insights from theory and simulation into the quantum glass transition that surprisingly reveal distinct regions where quantum fluctuations can either promote or inhibit glass formation.Comment: Accepted for publication in Nature Physics. 22 pages, 3 figures, 1 Tabl

End-to-end Interpretable Learning of Non-blind Image Deblurring

Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates. We propose to precondition the Richardson solver using approximate inverse filters of the (known) blur and natural image prior kernels. Using convolutions instead of a generic linear preconditioner allows extremely efficient parameter sharing across the image, and leads to significant gains in accuracy and/or speed compared to classical FFT and conjugate-gradient methods. More importantly, the proposed architecture is easily adapted to learning both the preconditioner and the proximal operator using CNN embeddings. This yields a simple and efficient algorithm for non-blind image deblurring which is fully interpretable, can be learned end to end, and whose accuracy matches or exceeds the state of the art, quite significantly, in the non-uniform case.Comment: Accepted at ECCV2020 (poster

Does physical activity counselling enhance the effects of a pedometer-based intervention over the long-term : 12-month findings from the Walking for Wellbeing in the West study

Peer reviewedPublisher PD

MagneToRE: Mapping the 3-D Magnetic Structure of the Solar Wind Using a Large Constellation of Nanosatellites

Unlike the vast majority of astrophysical plasmas, the solar wind is accessible to spacecraft, which for decades have carried in-situ instruments for directly measuring its particles and fields. Though such measurements provide precise and detailed information, a single spacecraft on its own cannot disentangle spatial and temporal fluctuations. Even a modest constellation of in-situ spacecraft, though capable of characterizing fluctuations at one or more scales, cannot fully determine the plasma’s 3-D structure. We describe here a concept for a new mission, the Magnetic Topology Reconstruction Explorer (MagneToRE), that would comprise a large constellation of in-situ spacecraft and would, for the first time, enable 3-D maps to be reconstructed of the solar wind’s dynamic magnetic structure. Each of these nanosatellites would be based on the CubeSat form-factor and carry a compact fluxgate magnetometer. A larger spacecraft would deploy these smaller ones and also serve as their telemetry link to the ground and as a host for ancillary scientific instruments. Such an ambitious mission would be feasible under typical funding constraints thanks to advances in the miniaturization of spacecraft and instruments and breakthroughs in data science and machine learning