54 research outputs found
The evidence for automated grading in diabetic retinopathy screening
Peer reviewedPostprin
Can a single image processing algorithm work equally well across all phases of DCE-MRI?
Image segmentation and registration are said to be challenging when applied
to dynamic contrast enhanced MRI sequences (DCE-MRI). The contrast agent causes
rapid changes in intensity in the region of interest and elsewhere, which can
lead to false positive predictions for segmentation tasks and confound the
image registration similarity metric. While it is widely assumed that contrast
changes increase the difficulty of these tasks, to our knowledge no work has
quantified these effects. In this paper we examine the effect of training with
different ratios of contrast enhanced (CE) data on two popular tasks:
segmentation with nnU-Net and Mask R-CNN and registration using VoxelMorph and
VTN. We experimented further by strategically using the available datasets
through pretraining and fine tuning with different splits of data. We found
that to create a generalisable model, pretraining with CE data and fine tuning
with non-CE data gave the best result. This interesting find could be expanded
to other deep learning based image processing tasks with DCE-MRI and provide
significant improvements to the models performance
Improving the economic value of photographic screening for optical coherence tomography-detectable macular oedema : a prospective, multicentre, UK study
Peer reviewedPublisher PD
Understanding stakeholder interactions in urban partnerships
This paper aims to better understand urban partnerships through the nature of the interactions between their stakeholders. Following a review of approaches to stakeholder arrangements in urban partnerships, which draws on a variety of literatures, including strategic management, public administration, urban studies and geography, the paper presents results of an action-case study undertaken in an urban partnership context – namely, Houldsworth Village Partnership (HVP) – within the Greater Manchester region of the UK. The findings begin by classifying HVP stakeholders along broad sectoral lines, before moving to examine, through a thematic analysis of data, the influences on their interactions in terms of ‘process enablers’ and ‘inhibitors’. This leads to a schema, whereby HVP stakeholder interactions are conceptualized on the dual continua of attitude and behavior. The schema provides a theoretical contribution by offering an understanding of stakeholders' dynamic interplay within an urban partnership context, and a means of classifying such stakeholders beyond their individual/organizational characteristics or sectoral affiliations
Effects of oxidized and reduced forms of methylthioninium in two transgenic mouse tauopathy models
Acknowledgements The authors acknowledge the contributions of Bettina Seelhorst (histological analysis), Anna Thoma (animal care), Marlene Arthur (animal dosing) and Pierre-Henri Moreau (experimental discussions). This work was supported by TauRx Therapeutics Ltd., Singapore.Peer reviewedPublisher PD
HMTM-Mediated Enhancement of Brain Bioenergetics in a Mouse Tauopathy Model Is Blocked by Chronic Administration of Rivastigmine
Funding: This study was sponsored by WisTa Laboratories Ltd., Singapore. (grant PAR1577).Peer reviewedPublisher PD
Quality assurance in diabetic retinal screening in South Africa
Background. Diabetic retinopathy (DR) is an important biomarker for microvascular disease and blindness. Digital fundus photography is a cost-effective way of screening for DR. Access to DR screening is difficult for many South Africans with diabetes.Objective. To perform external quality assurance (EQA) on graders registered in the Ophthalmological Society of South Africa DR screening programme.Methods. Graders registered on the South African (SA) Diabetic Register website were invited to participate in the study. The Scottish EQA software system was used to enable on-line grading of 100 retinal photographs. Expert National Health Service graders provided the consensus expert grading for the image set.Results. Two hundred and sixty-one participants completed the EQA process, including nine ophthalmologists, 243 optometrists, and nine other graders. A wide range of outcomes were demonstrated, with a mean sensitivity of 0.905 (range 0.286 - 1.000) and mean specificity of 0.507 (0.000 - 0.935). The mean diagnostic odds ratio was calculated to be 12.3 (range 0.147 - 148.2).Conclusions. This is the first quality assurance study conducted with SA healthcare professionals. The outcomes are of interest to all stakeholders dealing with the diabetes epidemic. The disparity in grader performance indicates room for improvement. The results demonstrate a high referral rate to ophthalmology, suggesting that on average graders are performing safely, but with a high number of inappropriate referrals
The role of noise in denoising models for anomaly detection in medical images
Pathological brain lesions exhibit diverse appearance in brain images, in
terms of intensity, texture, shape, size, and location. Comprehensive sets of
data and annotations are difficult to acquire. Therefore, unsupervised anomaly
detection approaches have been proposed using only normal data for training,
with the aim of detecting outlier anomalous voxels at test time. Denoising
methods, for instance classical denoising autoencoders (DAEs) and more recently
emerging diffusion models, are a promising approach, however naive application
of pixelwise noise leads to poor anomaly detection performance. We show that
optimization of the spatial resolution and magnitude of the noise improves the
performance of different model training regimes, with similar noise parameter
adjustments giving good performance for both DAEs and diffusion models. Visual
inspection of the reconstructions suggests that the training noise influences
the trade-off between the extent of the detail that is reconstructed and the
extent of erasure of anomalies, both of which contribute to better anomaly
detection performance. We validate our findings on two real-world datasets
(tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain
CT), showing good detection on diverse anomaly appearances. Overall, we find
that a DAE trained with coarse noise is a fast and simple method that gives
state-of-the-art accuracy. Diffusion models applied to anomaly detection are as
yet in their infancy and provide a promising avenue for further research.Comment: Submitted to Medical Image Analysis special issue for MIDL 202
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