Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc

Ambient-temperature incorporated hydrogen in Nb:SrTiO₃ single crystals

2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Exploring Flaring Behaviour on Low Mass Stars, Solar-type Stars and the Sun

We report on our project to study the activity in both the Sun and low mass stars. Utilising high cadence, Hα observations of a filament eruption made using the CRISP spectropolarimeter mounted on the Swedish Solar Telescope has allowed us to determine 3D velocity maps of the event. To gain insight into the physical mechanism which drives the event we have qualitatively compared our observation to a 3D MHD reconnection model. Solar-type and low mass stars can be highly active producing flares with energies exceeding erg. Using K2 and TESS data we find no correlation between the number of flares and the rotation phase which is surprising. Our solar flare model can be used to aid our understanding of the origin of flares in other stars. By scaling up our solar model to replicate observed stellar flare energies, we investigate the conditions needed for such high energy flares

Cell geometry across the ring structure of Sitka spruce.

For wood to be used to its full potential as an engineering material, it is necessary to quantify links between its cell geometry and the properties it exhibits at bulk scale. Doing so will make it possible to predict timber properties crucial to engineering, such as mechanical strength and stiffness, and the resistance to fluid flow, and to inform strategies to improve those properties as required, as well as to measure the effects of interventions such as genetic manipulation and chemical modification. Strength, stiffness and permeability of timber all derive from the geometry of its cells, and yet current practice is to predict them based on properties, such as bulk density, that do not directly describe the cell structure. This work explores links between micro-computed tomography data for structural-size pieces of wood, which show the variation of porosity across the wood's ring structure, and high-resolution tomography showing the geometry of the cells, from which we measure cell length, lumen area, porosity, cell wall thickness and the number density of cells. High-resolution scans, while informative, are time-consuming and expensive to run on a large number of samples at the scale of building components. By scanning the same volume of timber at both low and high resolutions (high-resolution scans over a near-continuous volume of timber of approx. 20 mm3 at 15 μm3 per voxel), we are able to demonstrate correlations between the measurements at the two different resolutions, reveal the physical basis for these correlations, and demonstrate that the data from the low-resolution scan can be used to estimate the variation in (small-scale) cell geometry throughout a structural-size piece of wood.This work was funded in major part by a Leverhulme Trust Programme Grant. The X-ray imaging work was supported by the Advanced Imaging of Materials (AIM) facility (EPSRC Grant No. EP/M028267/1), the European Social Fund (ESF) through the European Union’s Convergence programme administered by the Welsh Government

The Rydberg-Atom-Cavity Axion Search

We report on the present progress in development of the dark matter axion search experiment with Rydberg-atom-cavity detectors in Kyoto, CARRACK I and CARRACK II. The axion search has been performed with CARRACK I in the 8 % mass range around $10 \mu {\rm eV}$, and CARRACK II is now ready for the search in the wide range $2 \mu {\rm eV} - 50 \mu {\rm eV}$. We have also developed quantum theoretical calculations on the axion-photon-atom system in the resonant cavity in order to estimate precisely the detection sensitivity for the axion signal. Some essential features on the axion-photon-atom interaction are clarified, which provide the optimum experimental setup for the axion search.Comment: 8 pages, 2 figures, Invited talk presented at the Dark2000, Heidelberg, Germany,10-15 July, 200

Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial Behavior Analysi

Semantically Selective Augmentation for Deep Compact Person Re-Identification

We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks. In particular, we propose to augment limited training data via sampling from a deep convolutional generative adversarial network (DCGAN), whose discriminator is constrained by a semantic classifier to explicitly control the domain specificity of the generation process. Thereby, we encode information in the classifier network which can be utilized to steer adversarial synthesis, and which fuels our CondenseNet ID-network training. We provide a quantitative and qualitative analysis of the approach and its variants on a number of datasets, obtaining results that outperform the state-of-the-art on the LIMA dataset for long-term monitoring in indoor living spaces

Effects of Stellar Feedback on Stellar and Gas Kinematics of Star-forming Galaxies at 0.6 < z < 1.0

Recent zoom-in cosmological simulations have shown that stellar feedback can flatten the inner density profile of the dark matter halo in low-mass galaxies. A correlation between the stellar/gas velocity dispersion (σ star, σ gas) and the specific star formation rate (sSFR) is predicted as an observational test of the role of stellar feedback in re-shaping the dark matter density profile. In this work we test the validity of this prediction by studying a sample of star-forming galaxies at 0.6 < z < 1.0 from the LEGA-C survey, which provides high signal-to-noise measurements of stellar and gas kinematics. We find that a weak but significant correlation between σ star (and σ gas) and sSFR indeed exists for galaxies in the lowest mass bin (M ∗ ∼ 1010 M o˙). This correlation, albeit with a ∼35% scatter, holds for different tracers of star formation, and becomes stronger with redshift. This result generally agrees with the picture that at higher redshifts star formation rate was generally higher, and galaxies at M ∗ ≲ 1010 M o˙ have not yet settled into a disk. As a consequence, they have shallower gravitational potentials more easily perturbed by stellar feedback. The observed correlation between σ star (and σ gas) and sSFR supports the scenario predicted by cosmological simulations, in which feedback-driven outflows cause fluctuations in the gravitation potential that flatten the density profiles of low-mass galaxies

Evolutionary Games with Affine Fitness Functions: Applications to Cancer

We analyze the dynamics of evolutionary games in which fitness is defined as an affine function of the expected payoff and a constant contribution. The resulting inhomogeneous replicator equation has an homogeneous equivalent with modified payoffs. The affine terms also influence the stochastic dynamics of a two-strategy Moran model of a finite population. We then apply the affine fitness function in a model for tumor-normal cell interactions to determine which are the most successful tumor strategies. In order to analyze the dynamics of concurrent strategies within a tumor population, we extend the model to a three-strategy game involving distinct tumor cell types as well as normal cells. In this model, interaction with normal cells, in combination with an increased constant fitness, is the most effective way of establishing a population of tumor cells in normal tissue.Comment: The final publication is available at http://www.springerlink.com, http://dx.doi.org/10.1007/s13235-011-0029-

The effect of S-substitution at the O6-guanine site on the structure and dynamics of a DNA oligomer containing a G:T mismatch

The effect of S-substitution on the O6 guanine site of a 13-mer DNA duplex containing a G:T mismatch is studied using molecular dynamics. The structure, dynamic evolution and hydration of the S-substituted duplex are compared with those of a normal duplex, a duplex with Ssubstitution on guanine, but no mismatch and a duplex with just a G:T mismatch. The S-substituted mismatch leads to cell death rather than repair. One suggestion is that the G:T mismatch recognition protein recognises the S-substituted mismatch (GS:T) as G:T. This leads to a cycle of futile repair ending in DNA breakage and cell death. We find that some structural features of the helix are similar for the duplex with the G:T mismatch and that with the S-substituted mismatch, but differ from the normal duplex, notably the helical twist. These differences arise from the change in the hydrogen-bonding pattern of the base pair. However a marked feature of the S-substituted G:T mismatch duplex is a very large opening. This showed considerable variability. It is suggested that this enlarged opening would lend support to an alternative model of cell death in which the mismatch protein attaches to thioguanine and activates downstream damage-response pathways. Attack on the sulphur by reactive oxygen species, also leading to cell death, would also be aided by the large, variable opening