Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.This work is supported by the EPSRC First Grant scheme (grant ref no. EP/N023668/1) and partially funded under the 7th Framework Programme by the European Commission (TBIcare: http: //www.tbicare.eu/ ; CENTER-TBI: https://www.center-tbi.eu/). This work was further supported by a Medical Research Council (UK) Program Grant (Acute brain injury: heterogeneity of mechanisms, therapeutic targets and outcome effects [G9439390 ID 65883]), the UK National Institute of Health Research Biomedical Research Centre at Cambridge and Technology Platform funding provided by the UK Department of Health. KK is supported by the Imperial College London PhD Scholarship Programme. VFJN is supported by a Health Foundation/Academy of Medical Sciences Clinician Scientist Fellowship. DKM is supported by an NIHR Senior Investigator Award. We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs for our research

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the networks soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumors, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available

The impact of COVID-19 on anaesthesia and critical care services in the UK: a serial service evaluation

Between October 2020 and January 2021, we conducted three national surveys to track anaesthetic, surgical and critical care activity during the second COVID-19 pandemic wave in the UK. We surveyed all NHS hospitals where surgery is undertaken. Response rates, by round, were 64%, 56% and 51%. Despite important regional variations, the surveys showed increasing systemic pressure on anaesthetic and peri-operative services due to the need to support critical care pandemic demands. During Rounds 1 and 2, approximately one in eight anaesthetic staff were not available for anaesthetic work. Approximately one in five operating theatres were closed and activity fell in those that were open. Some mitigation was achieved by relocation of surgical activity to other locations. Approximately one-quarter of all surgical activity was lost, with paediatric and non-cancer surgery most impacted. During January 2021, the system was largely overwhelmed. Almost one-third of anaesthesia staff were unavailable, 42% of operating theatres were closed, national surgical activity reduced to less than half, including reduced cancer and emergency surgery. Redeployed anaesthesia staff increased the critical care workforce by 125%. Three-quarters of critical care units were so expanded that planned surgery could not be safely resumed. At all times, the greatest resource limitation was staff. Due to lower response rates from the most pressed regions and hospitals, these results may underestimate the true impact. These findings have important implications for understanding what has happened during the COVID-19 pandemic, planning recovery and building a system that will better respond to future waves or new epidemics

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network’s soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumors, and ischemic stroke. We improve on the state-of-theart for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly availabl

Resource-sharing in multiple component working memory

Working memory research often focuses on measuring the capacity of the system and how it relates to other cognitive abilities. However, research into the structure of working memory is less concerned with an overall capacity measure but rather with the intricacies of underlying components and their contribution to different tasks. A number of models of working memory structure have been proposed, each with different assumptions and predictions, but none of which adequately accounts for the full range of data in the working memory literature. We report 2 experiments that investigated the effects of load manipulations on dual-task verbal temporary memory and spatial processing. Crucially, we manipulated cognitive load around the measured memory span of each individual participant. We report a clear effect of increasing memory load on processing accuracy, but only when memory load is increased above each participant’s measured memory span. However, increasing processing load did not affect memory performance. We argue that immediate verbal memory may rely both on a temporary phonological store and on activated traces in long-term memory, with the latter deployed to support memory performance for supraspan lists and when a high memory load is coupled with a processing task. We propose that future research should tailor the load manipulations to the capacities of individual participants and suggest that contrasts between models of working memory may be more apparent than real

Fetal in vivo continuous cardiovascular function during chronic hypoxia.

Although the fetal cardiovascular defence to acute hypoxia and the physiology underlying it have been established for decades, how the fetal cardiovascular system responds to chronic hypoxia has been comparatively understudied. We designed and created isobaric hypoxic chambers able to maintain pregnant sheep for prolonged periods of gestation under controlled significant (10% O2) hypoxia, yielding fetal mean P(aO2) levels (11.5 ± 0.6 mmHg) similar to those measured in human fetuses of hypoxic pregnancy. We also created a wireless data acquisition system able to record fetal blood flow signals in addition to fetal blood pressure and heart rate from free moving ewes as the hypoxic pregnancy is developing. We determined in vivo longitudinal changes in fetal cardiovascular function including parallel measurement of fetal carotid and femoral blood flow and oxygen and glucose delivery during the last third of gestation. The ratio of oxygen (from 2.7 ± 0.2 to 3.8 ± 0.8; P < 0.05) and of glucose (from 2.3 ± 0.1 to 3.3 ± 0.6; P < 0.05) delivery to the fetal carotid, relative to the fetal femoral circulation, increased during and shortly after the period of chronic hypoxia. In contrast, oxygen and glucose delivery remained unchanged from baseline in normoxic fetuses. Fetal plasma urate concentration increased significantly during chronic hypoxia but not during normoxia (Δ: 4.8 ± 1.6 vs. 0.5 ± 1.4 μmol l(-1), P<0.05). The data support the hypotheses tested and show persisting redistribution of substrate delivery away from peripheral and towards essential circulations in the chronically hypoxic fetus, associated with increases in xanthine oxidase-derived reactive oxygen species.This work was supported by the British Heart Foundation.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1113/JP27109

No Clinically Relevant Effect of Heart Rate Increase and Heart Rate Recovery During Exercise on Cardiovascular Disease: A Mendelian Randomization Analysis

Background: Reduced heart rate (HR) increase (HRI), recovery (HRR), and higher resting HR are associated with cardiovascular (CV) disease, but causal inferences have not been deduced. We investigated causal effects of HRI, HRR, and resting HR on CV risk, all-cause mortality (ACM), atrial fibrillation (AF), coronary artery disease (CAD), and ischemic stroke (IS) using Mendelian Randomization. Methods: 11 variants for HRI, 11 for HRR, and two sets of 46 and 414 variants for resting HR were obtained from four genome-wide association studies (GWASs) on UK Biobank. We performed a lookup on GWASs for CV risk and ACM in UK Biobank (N = 375,367, 5.4% cases and N = 393,165, 4.4% cases, respectively). For CAD, AF, and IS, we used publicly available summary statistics. We used a random-effects inverse-variance weighted (IVW) method and sensitivity analyses to estimate causality. Results: IVW showed a nominally significant effect of HRI on CV events (odds ratio [OR] = 1.0012, P = 4.11 × 10–2) and on CAD and AF. Regarding HRR, IVW was not significant for any outcome. The IVW method indicated statistically significant associations of resting HR with AF (OR = 0.9825, P = 9.8 × 10–6), supported by all sensitivity analyses, and a nominally significant association with IS (OR = 0.9926, P = 9.82 × 10–3). Conclusion: Our findings suggest no strong evidence of an association between HRI and HRR and any outcome and confirm prior work reporting a highly significant effect of resting HR on AF. Future research is required to explore HRI and HRR associations further using more powerful predictors, when available

Bestrophin1: A Gene that Causes Many Diseases

Bestrophinopathies are a group of clinically distinct inherited retinal dystrophies that lead to the gradual loss of vision in and around the macular area. There are no treatments for patients suffering from bestrophinopathies, and no measures can be taken to prevent visual deterioration in those who have inherited disease-causing mutations. Bestrophinopathies are caused by mutations in the Bestrophin1 gene (BEST1), a protein found exclusively in the retinal pigment epithelial (RPE) cells of the eye. Mutations in BEST1 affect the function of the RPE leading to the death of overlying retinal cells and subsequent vision loss. The pathogenic mechanisms arising from BEST1 mutations are still not fully understood, and it is not clear how mutations in BEST1 lead to diseases with distinct clinical features. This chapter discusses BEST1, the use of model systems to investigate the effects of mutations and the potential to investigate individual bestrophinopathies using induced pluripotent stem cells

Deep-water Tectono-Stratigraphy at a Plate Boundary Constrained by Large N-Detrital Zircon and Micropaleontological Approaches: Peninsular Ranges Forearc, Baja California, Mexico

The distribution of sedimentary systems on Earth’s surface is intimately linked to tectonics, therefore, at plate boundaries the stratigraphic archive can unlock the timing and style of tectonism and relative plate motions. Using large-n detrital zircon and micropaleontological analyses, tied to field mapping and data collection, we unravel the timing of strike-slip motion and its influence on the development of a Cretaceous submarine canyon on a long-lived oblique-convergent margin. Structural analysis demonstrates that the canyon bedrock, composed of fluvial rocks (La Bocana Roja Fm., of maximum depositional age (MDA): 93.6±1.1 Ma), underwent both syn- and post-depositional contractional and extensional deformation during the Cenomanian-Turonian in response to dextral strike-slip movement. Relative sea-level rise associated with basin subsidence and hinterland uplift was coincident with incision and fill of a submarine canyon system (Punta Baja Fm., MDA 87.1±1.5 Ma to 84.9±2.0 Ma), which exploited structural lineaments in the bedrock. The canyon was filled by sediment derived from an uplifted magmatic arc during the Coniacian to Santonian, most likely shed from erosional topography associated with plutonic intrusions to the NE. Structural data suggest that oblique dextral strike-slip motion on the Pacific margin controlled the development and location of submarine erosion, and had ended by the earliest Santonian, significantly earlier than previously estimated. Basinward tilting led to uplift, followed by transgression and wave ravinement of the canyon fill, which was then overlain by a shallow-marine to fluvial system. Thus, the canyon was cut, filled, buried, uplifted and rotated basinward, planed off through wave ravinement, and onlapped by shallow-marine to fluvial sediments within an 8 Myr period. Our findings, in part, reconcile contrasting basin evolution models for the Late Mesozoic Pacific margin