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OXSeg: Multidimensional Attention UNet-Based Lip Segmentation Using Semi-Supervised Lip Contours
Lip segmentation plays a crucial role in various domains, such as lip synchronization, lip-reading, and diagnostics. However, the effectiveness of supervised lip segmentation is constrained by the availability of lip contour in the training phase. A further challenge with lip segmentation is its reliance on image quality, lighting, and skin tone, leading to inaccuracies in the detected boundaries. To address these challenges, we propose a sequential lip segmentation method that integrates attention UNet and multidimensional input. We unravel the micro-patterns in facial images using local binary patterns to build multidimensional inputs. Subsequently, the multidimensional inputs are fed into sequential attention UNets, where the lip contour is reconstructed. We introduce a mask generation method that uses a few anatomical landmarks and estimates the complete lip contour to improve segmentation accuracy. This mask has been utilized in the training phase for lip segmentation. To evaluate the proposed method, we use facial images to segment the upper lips and subsequently assess lip-related facial anomalies in subjects with fetal alcohol syndrome (FAS). Using the proposed lip segmentation method, we achieved a mean dice score of 84.75%, and a mean pixel accuracy of 99.77% in upper lip segmentation. To further evaluate the method, we implemented classifiers to identify those with FAS. Using a generative adversarial network (GAN), we reached an accuracy of 98.55% in identifying FAS in one of the study populations. This method could be used to improve lip segmentation accuracy, especially around Cupid’s bow, and sheds light on distinct lip-related characteristics of FAS.
Cohort profile: characterisation, determinants, mechanisms and consequences of the long-term effects of COVID-19 - providing the evidence base for health care services (CONVALESCENCE) in the UK.
PurposeThe pathogenesis of the long-lasting symptoms which can follow an infection with the SARS-CoV-2 virus ('long covid') is not fully understood. The 'COroNaVirus post-Acute Long-term EffectS: Constructing an evidENCE base' (CONVALESCENCE) study was established as part of the Longitudinal Health and Wellbeing COVID-19 UK National Core Study. We performed a deep phenotyping case-control study nested within two cohorts (the Avon Longitudinal Study of Parents and Children and TwinsUK) as part of CONVALESCENCE.ParticipantsFrom September 2021 to May 2023, 349 participants attended the CONVALESCENCE deep phenotyping clinic at University College London. Four categories of participants were recruited: cases of long covid (long covid(+)/SARS-CoV-2(+)), alongside three control groups: those with neither long covid symptoms nor evidence of prior COVID-19 (long covid(-)/SARS-CoV-2(-); control group 1), those who self-reported COVID-19 and had evidence of SARS-CoV-2 infection, but did not report long covid (long covid(-)/SARS-CoV-2(+); control group 2) and those who self-reported persistent symptoms attributable to COVID-19 but no evidence of SARS-CoV-2 infection (long covid(+)/SARS-CoV-2(-); control group 3). Remote wearable measurements were performed up until February 2024.Findings to dateThis cohort profile describes the baseline characteristics of the CONVALESCENCE cohort. Of the 349 participants, 141 (53±15 years old; 21 (15%) men) were cases, 89 (55±16 years old; 11 (12%) men) were in control group 1, 75 (49±15 years old; 25 (33%) men) were in control group 2 and 44 (55±16 years old; 9 (21%) men) were in control group 3.Future plansThe study aims to use a multiorgan score calculated as the cumulative total for each of nine domains (ie, lung, vascular, heart, kidney, brain, autonomic function, muscle strength, exercise capacity and physical performance). The availability of data preceding acute COVID-19 infection in cohorts may help identify the consequences of infection independent of pre-existing subclinical disease and also provide evidence of determinants that influence the development of long covid.
The developing Human Connectome Project fetal functional MRI release: Methods and data structures
Recent advances in fetal fMRI present a new opportunity for neuroscience to study functional human brain connectivity at the time of its emergence. Progress in the field, however, has been hampered by the lack of openly available datasets that can be exploited by researchers across disciplines to develop methods that would address the unique challenges associated with imaging and analysing functional brain in utero, such as unconstrained head motion, dynamically evolving geometric distortions, or inherently low signal-to-noise ratio. Here we describe the developing Human Connectome Project’s release of the largest open access fetal fMRI dataset to date, containing 275 scans from 255 foetuses and spanning the period of 20.86 to 38.29 post-menstrual weeks. We present a systematic approach to its pre-processing, implementing multi-band soft SENSE reconstruction, dynamic distortion corrections via phase unwrapping method, slice-to-volume reconstruction and a tailored temporal filtering model, with attention to the prominent sources of structured noise in the in utero fMRI. The dataset is accompanied with an advanced registration infrastructure, enabling group-level data fusion, and contains outputs from the main intermediate processing steps. This allows for various levels of data exploration by the imaging and neuroscientific community, starting from the development of robust pipelines for anatomical and temporal corrections to methods for elucidating the development of functional connectivity in utero. By providing a high-quality template for further method development and benchmarking, the release of the dataset will help to advance fetal fMRI to its deserved and timely place at the forefront of the efforts to build a life-long connectome of the human brain.
Individualised prediction of longitudinal change in multimodal brain imaging.
It remains largely unknown whether individualised longitudinal changes of brain imaging features can be predicted based only on the baseline brain images. This would be of great value, for example, for longitudinal data imputation, longitudinal brain-behaviour associations, and early prediction of brain-related diseases. We explore this possibility using longitudinal data of multiple modalities from UK Biobank brain imaging, with around 3,500 subjects. As baseline and follow-up images are generally similar in the case of short follow-up time intervals (e.g., 2 years), a simple copy of the baseline image may have a very good prediction performance. Therefore, for the first time, we propose a new mathematical framework for guiding the longitudinal prediction of brain images, providing answers to fundamental questions: (1) what is a suitable definition of longitudinal change; (2) how to detect the existence of changes; (3) what is the "null" prediction performance; and (4) can we distinguish longitudinal change prediction from simple data denoising. Building on these, we designed a deep U-Net based model for predicting longitudinal changes in multimodal brain images. Our results show that the proposed model can predict to a modest degree individualised longitudinal changes in almost all modalities, and outperforms other potential models. Furthermore, compared with the true longitudinal changes computed from real data, the predicted longitudinal changes have a similar or even improved accuracy in predicting subjects' non-imaging phenotypes, and have a high between-subject discriminability. Our study contributes a new theoretical framework for longitudinal brain imaging studies, and our results show the potential for longitudinal data imputation, along with highlighting several caveats when performing longitudinal data analysis.
The role of untranslated region variants in Mendelian disease: a review.
Untranslated regions (UTRs) flank the protein-coding sequence of a gene. 5'UTR and 3' UTR sequences mediate post-transcriptional regulation via linear and structural elements, controlling RNA stability, cellular localisation and the rate of protein translation. Variants within both 5' and 3' UTRs have been shown to cause disease through a variety of diverse mechanisms. However, for these variants to be routinely annotated and interpreted in clinical genetic testing, we need a better understanding of these regions and the spectrum of disease-causing variants within them. In this review, we systematically assess previously identified Mendelian disease-causing variants within UTRs and catalogue their underlying mechanisms. With genome sequencing becoming readily available and increasingly incorporated in diagnostic settings, this review will provide a valuable resource for the consideration and interpretation of UTR variants.
Statistical inference for same data meta-analysis in neuroimaging multiverse analyzes
Researchers using task-functional magnetic resonance imaging (fMRI) data have access to a wide range of analysis tools to model brain activity. If not accounted for properly, this plethora of analytical approaches can lead to an inflated rate of false positives and contribute to the irreproducibility of neuroimaging findings. Multiverse analyses are a way to systematically explore pipeline variations on a given dataset. We focus on the setting where multiple statistic maps are produced as an output of a set of analyses originating from a single dataset. However, having multiple outputs for the same research question—corresponding to different analytical approaches—makes it especially challenging to draw conclusions and interpret the findings. Meta-analysis is a natural approach to extract consensus inferences from these maps, yet the traditional assumption of independence among input datasets does not hold here. In this work, we consider a suite of methods to conduct meta-analysis in the multiverse setting, which we call same data meta-analysis (SDMA), accounting for inter-pipeline dependence among the results. First, we assessed the validity of these methods in simulations. Then, we tested them on the multiverse outputs of two real-world multiverse analyses: “NARPS”, a multiverse study originating from the same dataset analyzed by 70 different teams, and “HCP Young Adult”, a more homogeneous multiverse analysis using 24 different pipelines analyzed by the same team. Our findings demonstrate the validity of our proposed SDMA models under inter-pipeline dependence, and provide an array of options, with different levels of relevance, for the analysis of multiverse outputs.