Uncertainty categories in medical image segmentation: a study of source-related diversity

Measuring uncertainties in the output of a deep learning method is useful in several ways, such as in assisting with interpretation of the outputs, helping build confidence with end users, and for improving the training and performance of the networks. Several different methods have been proposed to estimate uncertainties, including those from epistemic (relating to the model used) and aleatoric (relating to the data) sources using test-time dropout and augmentation, respectively. Not only are these uncertainty sources different, but they are governed by parameter settings (e.g., dropout rate or type and level of augmentation) that establish even more distinct uncertainty categories. This work investigates how different the uncertainties are from these categories, for magnitude and spatial pattern, to empirically address the question of whether they provide usefully distinct information that should be captured whenever uncertainties are used. We take the well characterised BraTS challenge dataset to demonstrate that there are substantial differences in both magnitude and spatial pattern of uncertainties from the different categories, and discuss the implications of these in various use cases

Planting the Seeds of Cross-Disciplinary Teaching & Assessment

Peer reviewedPublisher PD

SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis

To represent the biological variability of clinical neuroimaging populations, it is vital to be able to combine data across scanners and studies. However, different MRI scanners produce images with different characteristics, resulting in a domain shift known as the `harmonisation problem'. Additionally, neuroimaging data is inherently personal in nature, leading to data privacy concerns when sharing the data. To overcome these barriers, we propose an Unsupervised Source-Free Domain Adaptation (SFDA) method, SFHarmony. Through modelling the imaging features as a Gaussian Mixture Model and minimising an adapted Bhattacharyya distance between the source and target features, we can create a model that performs well for the target data whilst having a shared feature representation across the data domains, without needing access to the source data for adaptation or target labels. We demonstrate the performance of our method on simulated and real domain shifts, showing that the approach is applicable to classification, segmentation and regression tasks, requiring no changes to the algorithm. Our method outperforms existing SFDA approaches across a range of realistic data scenarios, demonstrating the potential utility of our approach for MRI harmonisation and general SFDA problems. Our code is available at \url{https://github.com/nkdinsdale/SFHarmony}

Serum and urine vitamin D metabolite analysis in early preeclampsia

Vitamin D-deficiency is common in pregnant women, and may contribute to adverse events in pregnancy such as preeclampsia (PET). To date, studies of vitamin D and PET have focused primarily on serum concentrations vitamin D, 25-hydroxyvitamin D3 (25(OH)D3) later in pregnancy. The aim here was to determine whether a more comprehensive analysis of vitamin D metabolites earlier in pregnancy could provide predictors of PET. Using samples from the SCOPE pregnancy cohort, multiple vitamin D metabolites were quantified by liquid chromatography-tandem mass spectrometry in paired serum and urine prior to the onset of PET symptoms. Samples from 50 women at pregnancy week 15 were analysed, with 25 (50%) developing PET by the end of the pregnancy and 25 continuing with uncomplicated pregnancy. Paired serum and urine from non-pregnant women (n=9) of reproductive age were also used as a control. Serum concentrations of 25(OH)D3, 25(OH)D2, 1,25(OH)2D3, 24,25(OH)2D3, and 3-epi-25(OH)D3 were measured and showed no significant difference between women with uncomplicated pregnancies and those developing PET. As previously reported, serum 1,25(OH)2D3 was higher in all pregnant women (in the second trimester), but serum 25(OH)D2 was also higher compared to non-pregnant women. In urine, 25(OH)D3 and 24,25(OH)2D3 were quantifiable, with both metabolites demonstrating significantly lower (p&lt;0.05) concentrations of both of these metabolites in those destined to develop PET. These data indicate that analysis of urinary metabolites provides an additional insight into vitamin D and the kidney, with lower urinary 25(OH)D3, and 24,25(OH)2D3 excretion being an early indicator of a predisposition towards developing PET.</p

O-mannosylation in Candida albicans enables development of interkingdom biofilm communities

Peer reviewedPublisher PD

Mentalizing the body: : spatial and social cognition in anosognosia for hemiplegia

© The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] right-hemisphere damage, a specific disorder of motor awareness can occur called anosognosia for hemiplegia, i.e. the denial of motor deficits contralateral to a brain lesion. The study of anosognosia can offer unique insights into the neurocognitive basis of awareness. Typically, however, awareness is assessed as a first person judgement and the ability of patients to think about their bodies in more 'objective' (third person) terms is not directly assessed. This may be important as right-hemisphere spatial abilities may underlie our ability to take third person perspectives. This possibility was assessed for the first time in the present study. We investigated third person perspective taking using both visuospatial and verbal tasks in right-hemisphere stroke patients with anosognosia (n = 15) and without anosognosia (n = 15), as well as neurologically healthy control subjects (n = 15). The anosognosic group performed worse than both control groups when having to perform the tasks from a third versus a first person perspective. Individual analysis further revealed a classical dissociation between most anosognosic patients and control subjects in mental (but not visuospatial) third person perspective taking abilities. Finally, the severity of unawareness in anosognosia patients was correlated to greater impairments in such third person, mental perspective taking abilities (but not visuospatial perspective taking). In voxel-based lesion mapping we also identified the lesion sites linked with such deficits, including some brain areas previously associated with inhibition, perspective taking and mentalizing, such as the inferior and middle frontal gyri, as well as the supramarginal and superior temporal gyri. These results suggest that neurocognitive deficits in mental perspective taking may contribute to anosognosia and provide novel insights regarding the relation between self-awareness and social cognition.Peer reviewe

Increasing the detectability of external influence on precipitation by correcting feature location in GCMs

Understanding how precipitation varies as the climate changes is essential to determining the true impact of global warming. This is a difficult task not only due to the large internal variability observed in precipitation but also because of a limited historical record and large biases in simulations of precipitation by general circulation models (GCMs). Here we make use of a technique that spatially and seasonally transforms GCM fields to reduce location biases and investigate the potential of this bias correction to study historical changes. We use two versions of this bias correction—one that conserves intensities and another that conserves integrated precipitation over transformed areas. Focussing on multimodel ensemble means, we find that both versions reduce RMS error in the historical trend by approximately 11% relative to the Global Precipitation Climatology Project (GPCP) data set. By regressing GCMs' historical simulations of precipitation onto radiative forcings, we decompose these simulations into anthropogenic and natural time series. We then perform a simple detection and attribution study to investigate the impact of reducing location biases on detectability. A multiple ordinary least squares regression of GPCP onto the anthropogenic and natural time series, with the assumptions made, finds anthropogenic detectability only when spatial corrections are applied. The result is the same regardless of which form of conservation is used and without reducing the dimensionality of the fields beyond taking zonal means. While “detectability” is dependent both on the exact methodology and the confidence required, this nevertheless demonstrates the potential benefits of correcting location biases in GCMs when studying historical precipitation, especially in cases where a signal was previously undetectable

Diffusion-weighted MRI characteristics of the cerebral metastasis to brain boundary predicts patient outcomes.

DWI demonstrates changes in the tumor, across the tumor edge and in the peritumoral region which may not be visible on conventional MRI and this may be useful in predicting patient outcomes for operated cerebral metastases