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

Predicted impact of extending the screening interval for diabetic retinopathy:the Scottish Diabetic Retinopathy Screening programme

Peer reviewedPublisher PD

A reference standard for the measurement of macular oedema

Macular oedema is associated with several conditions that lead to blindness. Accurate measurement of macular thickness is important in order to follow disease progression and evaluate treatments. Four techniques are examined to determine the best reference standard for the detection and quantification of macular oedema: ultrasound, optical coherence tomography, the retinal thickness analyser, and the scanning laser ophthalmoscope. The three optical techniques have the highest resolution and sensitivity, in particular optical coherence tomography. Ultrasound can be useful where dense opacities preclude optical imaging

The value of digital imaging in diabetic retinopathy

Objectives To assess the performance of digital imaging, compared with other modalities, in screening for and monitoring the development of diabetic retinopathy. Design All imaging was acquired at a hospital assessment clinic. Subsequently, study optometrists examined the patients in their own premises. A subset of patients also had fluorescein angiography performed every 6 months. Setting Research clinic at the hospital eye clinic and optometrists' own premises. Participants Study comprised 103 patients who had type 1 diabetes mellitus, 481 had type 2 diabetes mellitus and two had secondary diabetes mellitus; 157 (26.8%) had some form of retinopathy ('any') and 58 (9.9%) had referable retinopathy. Interventions A repeat assessment was carried out of all patients 1 year after their initial assessment. Patients who had more severe forms of retinopathy were monitored more frequently for evidence of progression. Main outcome measures Detection of retinopathy, progression of retinopathy and determination of when treatment is required. Results Manual grading of 35-mm colour slides produced the highest sensitivity and specificity figures, with optometrist examination recording most false negatives. Manual and automated analysis of digital images had intermediate sensitivity. Both manual grading of 35-mm colour slides and digital images gave sensitivities of over 90% with few false positives. Digital imaging produced 50% fewer ungradable images than colour slides. This part of the study was limited as patients with the more severe levels of retinopathy opted for treatment. There was an increase in the number of microaneurysms in those patients who developed from mild to moderate. There was no difference between the turnover rate of either new or regressed microaneurysms for patients with mild or with sight-threatening retinopathy. It was not possible in this study to ascertain whether digital imaging systems determine when treatment is warranted. Conclusions In the context of a national screening programme for referable retinopathy, digital imaging is an effective method. In addition, technical failure rates are lower with digital imaging than conventional photography. Digital imaging is also a more sensitive technique than slit-lamp examination by optometrists. Automated grading can improve efficiency by correctly identifying just under half the population as having no retinopathy. Recommendations for future research include: investigating whether the nasal field is required for grading; a large screening programme is required to ascertain if automated grading can safely perform as a first-level grader; if colour improves the performance of grading digital images; investigating methods to ensure effective uptake in a diabetic retinopathy screening programme

The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme

Aim: To assess the efficacy of automated “disease/no disease” grading for diabetic retinopathy within a systematic screening programme. Methods: Anonymised images were obtained from consecutive patients attending a regional primary care based diabetic retinopathy screening programme. A training set of 1067 images was used to develop automated grading algorithms. The final software was tested using a separate set of 14406 images from 6722 patients. The sensitivity and specificity of manual and automated systems operating as “disease/no disease” graders (detecting poor quality images and any diabetic retinopathy) were determined relative to a clinical reference standard. Results: The reference standard classified 8.2% of the patients as having ungradable images (technical failures) and 62.5% as having no retinopathy. Detection of technical failures or any retinopathy was achieved by manual grading with 86.5% sensitivity (95% confidence interval 85.1 to 87.8) and 95.3% specificity (94.6 to 95.9) and by automated grading with 90.5% sensitivity (89.3 to 91.6) and 67.4% specificity (66.0 to 68.8). Manual and automated grading detected 99.1% and 97.9%, respectively, of patients with referable or observable retinopathy/maculopathy. Manual and automated grading detected 95.7% and 99.8%, respectively, of technical failures. Conclusion: Automated “disease/no disease” grading of diabetic retinopathy could safely reduce the burden of grading in diabetic retinopathy screening programmes

Cost-effectiveness of implementing automated grading within the national screening programme for diabetic retinopathy in Scotland

Aims: National screening programmes for diabetic retinopathy using digital photography and multi-level manual grading systems are currently being implemented in the UK. Here, we assess the cost-effectiveness of replacing first level manual grading in the National Screening Programme in Scotland with an automated system developed to assess image quality and detect the presence of any retinopathy. Methods: A decision tree model was developed and populated using sensitivity/specificity and cost data based on a study of 6722 patients in the Grampian region. Costs to the NHS, and the number of appropriate screening outcomes and true referable cases detected in 1 year were assessed. Results: For the diabetic population of Scotland (approximately 160 000), with prevalence of referable retinopathy at 4% (6400 true cases), the automated strategy would be expected to identify 5560 cases (86.9%) and the manual strategy 5610 cases (87.7%). However, the automated system led to savings in grading and quality assurance costs to the NHS of £201 600 per year. The additional cost per additional referable case detected (manual vs automated) totalled £4088 and the additional cost per additional appropriate screening outcome (manual vs automated) was £1990. Conclusions: Given that automated grading is less costly and of similar effectiveness, it is likely to be considered a cost-effective alternative to manual grading