34 research outputs found
FASTR: Using Local Structure Tensors as a Similarity Metric
AbstractWe describe a novel structural image descriptor for image registration called the Fractionally Anisotropic Structural Tensor Representation (FASTR), calculated from the local structural tensor (LST). The metric has several characteristics that are advantageous for multi-modality registration, such as not depending on absolute voxel intensities, and being insensitive to slowly varying in- tensity inhomogeneities across the image. This latter property is very useful, since many imaging modalities suffer from such artefacts. Registration accuracy is tested on both computed tomography (CT) to cone-beam CT (CBCT) rigid registration, and CT to magnetic resonance (MR) rigid registration. The performance is compared with Mutual Information (MI) metric and the Self Similarity Context (SSC) descriptor. The results show that, for images with significant intensity inhomogeneity, FASTR produced more accurate results than MI, and faster results than SSC. The results suggest FASTR gives similar benefits in images with intensity inhomogeneity, but at a fraction of the computation and memory demand
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
Deep learning detection of diabetic retinopathy in Scotland's diabetic eye screening programme
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
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