arXiv subjects

Chris Page

Publications and source records attributed to Chris Page.

3 recordsLinked to original sources

arXiv2026

Cluster emission and its impact on the r-process nucleosynthesis

Cluster emission is an exotic decay mode between alpha-decay and fission, in which a parent nucleus emits a cluster of nucleons heavier than an alpha-particle, but lighter than what is usually considered a fission fragment. The properties of cluster emission were investigated by analyzing five high-energy events detected in a spectrum of a mass A = 230 beam produced at ISOLDE (CERN). Under the assumption that these events were caused by cluster emission, the most likely parent-cluster pair responsible for the five high-energy events, was found to be $^{230}Ra$ emitting $^{22}O$, with a branching ratio of $(4.3$ +\- $1.9) \times 10^{-9}$. Four analytical formulas were used to estimate the partial half-lives of cluster emission for a group of neutron-rich nuclei. The decay rate was calculated for a selection of cluster nuclei for each parent isotope. The rates of all decay channels of cluster emission per parent nucleus were then included in calculations of the r-process nucleosynthesis in a neutron star merger in order to study the possible impact of cluster emission on the r-process nuclear production. The resulting isotopic abundance distributions were compared to those calculated for a case in which cluster emission was not considered. It was found that the inclusion of cluster emission decay rates from the simple analytical formulas available nowadays does not influence the results of the r-process nucleosynthesis.

nucl-ex↗

arXiv2019

Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

cs.CV↗

arXiv2018

Real-time Prediction of Segmentation Quality

Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image quality, artifacts or unexpected behaviour of black box algorithms. Being able to predict segmentation quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis. In this work, we propose two approaches of real-time automated quality control for cardiovascular MR segmentations using deep learning. First, we train a neural network on 12,880 samples to predict Dice Similarity Coefficients (DSC) on a per-case basis. We report a mean average error (MAE) of 0.03 on 1,610 test samples and 97% binary classification accuracy for separating low and high quality segmentations. Secondly, in the scenario where no manually annotated data is available, we train a network to predict DSC scores from estimated quality obtained via a reverse testing strategy. We report an MAE=0.14 and 91% binary classification accuracy for this case. Predictions are obtained in real-time which, when combined with real-time segmentation methods, enables instant feedback on whether an acquired scan is analysable while the patient is still in the scanner. This further enables new applications of optimising image acquisition towards best possible analysis results.

cs.CV↗