30 research outputs found
The Role of MRI Physics in Brain Segmentation CNNs: Achieving Acquisition Invariance and Instructive Uncertainties
Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases. It is important therefore to design algorithms that are not only robust to images of differing contrasts, but also be able to generalise well to unseen ones, with a quantifiable measure of uncertainty. In this paper we demonstrate the efficacy of a physics-informed, uncertainty-aware, segmentation network that employs augmentation-time MR simulations and homogeneous batch feature stratification to achieve acquisition invariance. We show that the proposed approach also accurately extrapolates to out-of-distribution sequence samples, providing well calibrated volumetric bounds on these. We demonstrate a significant improvement in terms of coefficients of variation, backed by uncertainty based volumetric validation
Hierarchical brain parcellation with uncertainty
Many atlases used for brain parcellation are hierarchically organised,
progressively dividing the brain into smaller sub-regions. However,
state-of-the-art parcellation methods tend to ignore this structure and treat
labels as if they are `flat'. We introduce a hierarchically-aware brain
parcellation method that works by predicting the decisions at each branch in
the label tree. We further show how this method can be used to model
uncertainty separately for every branch in this label tree. Our method exceeds
the performance of flat uncertainty methods, whilst also providing decomposed
uncertainty estimates that enable us to obtain self-consistent parcellations
and uncertainty maps at any level of the label hierarchy. We demonstrate a
simple way these decision-specific uncertainty maps may be used to provided
uncertainty-thresholded tissue maps at any level of the label tree.Comment: To be published in the MICCAI 2020 workshop: Uncertainty for Safe
Utilization of Machine Learning in Medical Imagin
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning
The ability to synthesise Computed Tomography images - commonly known as
pseudo CT, or pCT - from MRI input data is commonly assessed using an
intensity-wise similarity, such as an L2-norm between the ground truth CT and
the pCT. However, given that the ultimate purpose is often to use the pCT as an
attenuation map (-map) in Positron Emission Tomography Magnetic Resonance
Imaging (PET/MRI), minimising the error between pCT and CT is not necessarily
optimal. The main objective should be to predict a pCT that, when used as
-map, reconstructs a pseudo PET (pPET) which is as close as possible to
the gold standard PET. To this end, we propose a novel multi-hypothesis deep
learning framework that generates pCTs by minimising a combination of the
pixel-wise error between pCT and CT and a proposed metric-loss that itself is
represented by a convolutional neural network (CNN) and aims to minimise
subsequent PET residuals. The model is trained on a database of 400 paired
MR/CT/PET image slices. Quantitative results show that the network generates
pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT
(69.68HU) compared to a baseline CNN (66.25HU), but lead to significant
improvement in the PET reconstruction - 115a.u. compared to baseline 140a.u.Comment: Aceppted at SASHIMI201
Let's agree to disagree: learning highly debatable multirater labelling
Classification and differentiation of small pathological objects may greatly
vary among human raters due to differences in training, expertise and their
consistency over time. In a radiological setting, objects commonly have high
within-class appearance variability whilst sharing certain characteristics
across different classes, making their distinction even more difficult. As an
example, markers of cerebral small vessel disease, such as enlarged
perivascular spaces (EPVS) and lacunes, can be very varied in their appearance
while exhibiting high inter-class similarity, making this task highly
challenging for human raters. In this work, we investigate joint models of
individual rater behaviour and multirater consensus in a deep learning setting,
and apply it to a brain lesion object-detection task. Results show that jointly
modelling both individual and consensus estimates leads to significant
improvements in performance when compared to directly predicting consensus
labels, while also allowing the characterization of human-rater consistency.Comment: Accepted at MICCAI 201
Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study
Background
The SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility.
Methods
We did an ecological study to examine the association between the regional proportion of infections with the SARS-CoV-2 B.1.1.7 variant and reported symptoms, disease course, rates of reinfection, and transmissibility. Data on types and duration of symptoms were obtained from longitudinal reports from users of the COVID Symptom Study app who reported a positive test for COVID-19 between Sept 28 and Dec 27, 2020 (during which the prevalence of B.1.1.7 increased most notably in parts of the UK). From this dataset, we also estimated the frequency of possible reinfection, defined as the presence of two reported positive tests separated by more than 90 days with a period of reporting no symptoms for more than 7 days before the second positive test. The proportion of SARS-CoV-2 infections with the B.1.1.7 variant across the UK was estimated with use of genomic data from the COVID-19 Genomics UK Consortium and data from Public Health England on spike-gene target failure (a non-specific indicator of the B.1.1.7 variant) in community cases in England. We used linear regression to examine the association between reported symptoms and proportion of B.1.1.7. We assessed the Spearman correlation between the proportion of B.1.1.7 cases and number of reinfections over time, and between the number of positive tests and reinfections. We estimated incidence for B.1.1.7 and previous variants, and compared the effective reproduction number, Rt, for the two incidence estimates.
Findings
From Sept 28 to Dec 27, 2020, positive COVID-19 tests were reported by 36 920 COVID Symptom Study app users whose region was known and who reported as healthy on app sign-up. We found no changes in reported symptoms or disease duration associated with B.1.1.7. For the same period, possible reinfections were identified in 249 (0·7% [95% CI 0·6–0·8]) of 36 509 app users who reported a positive swab test before Oct 1, 2020, but there was no evidence that the frequency of reinfections was higher for the B.1.1.7 variant than for pre-existing variants. Reinfection occurrences were more positively correlated with the overall regional rise in cases (Spearman correlation 0·56–0·69 for South East, London, and East of England) than with the regional increase in the proportion of infections with the B.1.1.7 variant (Spearman correlation 0·38–0·56 in the same regions), suggesting B.1.1.7 does not substantially alter the risk of reinfection. We found a multiplicative increase in the Rt of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69) relative to pre-existing variants. However, Rt fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant.
Interpretation
The lack of change in symptoms identified in this study indicates that existing testing and surveillance infrastructure do not need to change specifically for the B.1.1.7 variant. In addition, given that there was no apparent increase in the reinfection rate, vaccines are likely to remain effective against the B.1.1.7 variant.
Funding
Zoe Global, Department of Health (UK), Wellcome Trust, Engineering and Physical Sciences Research Council (UK), National Institute for Health Research (UK), Medical Research Council (UK), Alzheimer's Society
PIMMS:Permutation Invariant Multi-Modal Segmentation
In a research context, image acquisition will often involve a pre-defined
static protocol and the data will be of high quality. If we are to build
applications that work in hospitals without significant operational changes in
care delivery, algorithms should be designed to cope with the available data in
the best possible way. In a clinical environment, imaging protocols are highly
flexible, with MRI sequences commonly missing appropriate sequence labeling
(e.g. T1, T2, FLAIR). To this end we introduce PIMMS, a Permutation Invariant
Multi-Modal Segmentation technique that is able to perform inference over sets
of MRI scans without using modality labels. We present results which show that
our convolutional neural network can, in some settings, outperform a baseline
model which utilizes modality labels, and achieve comparable performance
otherwise.Comment: Accepted at the 4th Workshop on Deep Learning in Medical Image
Analysis held at MICCAI201
A k-Space Model of Movement Artefacts: Application to Segmentation Augmentation and Artefact Removal
The Role of MRI Physics in Brain Segmentation CNNs:Achieving Acquisition Invariance and Instructive Uncertainties
Being able to adequately process and combine data arising from different
sites is crucial in neuroimaging, but is difficult, owing to site, sequence and
acquisition-parameter dependent biases. It is important therefore to design
algorithms that are not only robust to images of differing contrasts, but also
be able to generalise well to unseen ones, with a quantifiable measure of
uncertainty. In this paper we demonstrate the efficacy of a physics-informed,
uncertainty-aware, segmentation network that employs augmentation-time MR
simulations and homogeneous batch feature stratification to achieve acquisition
invariance. We show that the proposed approach also accurately extrapolates to
out-of-distribution sequence samples, providing well calibrated volumetric
bounds on these. We demonstrate a significant improvement in terms of
coefficients of variation, backed by uncertainty based volumetric validation.Comment: 10 pages, 3 figures, published in: Simulation and Synthesis in
Medical Imaging 6th International Workshop, SASHIMI 2021, Held in Conjunction
with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceeding