179 research outputs found
A quantitative mesoscale characterization of the mechanical behaviour of Ceramic Matrix Composites
An experimental micro-macro kinematic description of matrix cracking in unidirectionnal ceramic matrix composites is proposed. It has been enlighten by observations performed during an in situ tensile test in a Scanning Electron Microscope. The characterization of matrix crack nucleation, propagation and coalescence has been done with new parameters and related to the macroscopic behaviour
FLAIR-only joint volumetric analysis of brain lesions and atrophy in clinically isolated syndrome (CIS) suggestive of multiple sclerosis
Background:
MRI assessment in multiple sclerosis (MS) focuses on the presence of typical white matter (WM) lesions. Neurodegeneration characterised by brain atrophy is recognised in the research field as an important prognostic factor. It is not routinely reported clinically, in part due to difficulty in achieving reproducible measurements. Automated MRI quantification of WM lesions and brain volume could provide important clinical monitoring data. In general, lesion quantification relies on both T1 and FLAIR input images, while tissue volumetry relies on T1. However, T1-weighted scans are not routinely included in the clinical MS protocol, limiting the utility of automated quantification.
Objectives:
We address an aspect of this important translational challenge by assessing the performance of FLAIR-only lesion and brain segmentation, against a conventional approach requiring multi-contrast acquisition. We explore whether FLAIR-only grey matter (GM) segmentation yields more variability in performance compared with two-channel segmentation; whether this is related to field strength; and whether the results meet a level of clinical acceptability demonstrated by the ability to reproduce established biological associations.
Methods:
We used a multicentre dataset of subjects with a CIS suggestive of MS scanned at 1.5T and 3T in the same week. WM lesions were manually segmented by two raters, ‘manual 1′ guided by consensus reading of CIS-specific lesions and ‘manual 2′ by any WM hyperintensity. An existing brain segmentation method was adapted for FLAIR-only input. Automated segmentation of WM hyperintensity and brain volumes were performed with conventional (T1/T1 + FLAIR) and FLAIR-only methods.
Results:
WM lesion volumes were comparable at 1.5T between ‘manual 2′ and FLAIR-only methods and at 3T between ‘manual 2′, T1 + FLAIR and FLAIR-only methods. For cortical GM volume, linear regression measures between conventional and FLAIR-only segmentation were high (1.5T: α = 1.029, R2 = 0.997, standard error (SE) = 0.007; 3T: α = 1.019, R2 = 0.998, SE = 0.006). Age-associated change in cortical GM volume was a significant covariate in both T1 (p = 0.001) and FLAIR-only (p = 0.005) methods, confirming the expected relationship between age and GM volume for FLAIR-only segmentations.
Conclusions:
FLAIR-only automated segmentation of WM lesions and brain volumes were consistent with results obtained through conventional methods and had the ability to demonstrate biological effects in our study population. Imaging protocol harmonisation and validation with other MS phenotypes could facilitate the integration of automated WM lesion volume and brain atrophy analysis as clinical tools in radiological MS reporting
FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
In this paper, we present a generic deep convolutional neural network (DCNN)
for multi-class image segmentation. It is based on a well-established
supervised end-to-end DCNN model, known as U-net. U-net is firstly modified by
adding widely used batch normalization and residual block (named as BRU-net) to
improve the efficiency of model training. Based on BRU-net, we further
introduce a dynamically weighted cross-entropy loss function. The weighting
scheme is calculated based on the pixel-wise prediction accuracy during the
training process. Assigning higher weights to pixels with lower segmentation
accuracies enables the network to learn more from poorly predicted image
regions. Our method is named as feedback weighted U-net (FU-net). We have
evaluated our method based on T1- weighted brain MRI for the segmentation of
midbrain and substantia nigra, where the number of pixels in each class is
extremely unbalanced to each other. Based on the dice coefficient measurement,
our proposed FU-net has outperformed BRU-net and U-net with statistical
significance, especially when only a small number of training examples are
available. The code is publicly available in GitHub (GitHub link:
https://github.com/MinaJf/FU-net).Comment: Accepted for publication at International Conference on Image and
Graphics (ICIG 2019
Automated white matter hyperintensity segmentation using Bayesian Model Selection: assessment and correlations with cognitive change
Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer’s (AD) participants. Data were downloaded from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Magnetic resonance images from 30 control and 30 AD participants were selected to incorporate multiple scanners, and were semi-automatically segmented by 4 raters and BaMoS. Segmentations were assessed using volume correlation, Dice score, and other spatial metrics. Linear mixed-effect models were fitted to 180 control, 107 SMC, 320 EMCI, 171 LMCI and 151 AD participants separately in each group, with the outcomes being cognitive change (e.g. mini-mental state examination; MMSE), and BaMoS WMH, age, sex, race and education used as predictors. There was a high level of agreement between BaMoS’ WMH segmentation volumes and a consensus of rater segmentations, with a median Dice score of 0.74 and correlation coefficient of 0.96. BaMoS WMH predicted cognitive change in: control, EMCI, and SMC groups using MMSE; LMCI using clinical dementia rating scale; and EMCI using Alzheimer’s disease assessment scale-cognitive subscale (p < 0.05, all tests). BaMoS compares well to semi-automated segmentation, is robust to different WMH loads and scanners, and can generate volumes which predict decline. BaMoS can be applicable to further large-scale studies
Neuroimaging correlates of brain injury in Wilson's disease: a multimodal, whole-brain MRI study
Wilson's disease is an autosomal-recessive disorder of copper metabolism with neurological and hepatic presentations. Chelation therapy is used to 'de-copper' patients but neurological outcomes remain unpredictable. A range of neuroimaging abnormalities have been described and may provide insights into disease mechanisms, in addition to prognostic and monitoring biomarkers. Previous quantitative MRI analyses have focussed on specific sequences or regions of interest, often stratifying chronically-treated patients according to persisting symptoms as opposed to initial presentation. In this cross-sectional study, we performed a combination of unbiased, whole-brain analyses on T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and susceptibility-weighted imaging data from 40 prospectively-recruited patients with Wilson's disease (age range 16-68). We compared patients with neurological (n = 23) and hepatic (n = 17) presentations to determine the neuroradiological sequelae of the initial brain injury. We also subcategorized patients according to recent neurological status, classifying those with neurological presentations or deterioration in the preceding six months as having 'active' disease. This allowed us to compare patients with active (n = 5) and stable (n = 35) disease and identify imaging correlates for persistent neurological deficits and copper indices in chronically-treated, stable patients. Using a combination of voxel-based morphometry and region-of-interest volumetric analyses, we demonstrate that grey matter volumes are lower in the basal ganglia, thalamus, brainstem, cerebellum, anterior insula and orbitofrontal cortex when comparing patients with neurological and hepatic presentations. In chronically-treated, stable patients, the severity of neurological deficits correlated with grey matter volumes in similar, predominantly subcortical regions. In contrast, the severity of neurological deficits did not correlate with the volume of white matter hyperintensities, calculated using an automated lesion segmentation algorithm. Using tract-based spatial statistics, increasing neurological severity in chronically-treated patients was associated with decreasing axial diffusivity in white matter tracts whereas increasing serum non-caeruloplasmin-bound ('free') copper and active disease were associated with distinct patterns of increasing mean, axial and radial diffusivity. Whole-brain quantitative susceptibility mapping identified increased iron deposition in the putamen, cingulate and medial frontal cortices of patients with neurological presentations relative to those with hepatic presentations and neurological severity was associated with iron deposition in widespread cortical regions in chronically-treated patients. Our data indicate that composite measures of subcortical atrophy provide useful prognostic biomarkers, whereas abnormal mean, axial and radial diffusivity are promising monitoring biomarkers. Finally, deposition of brain iron in response to copper accumulation may directly contribute to neurodegeneration in Wilson's disease
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice
The Dice score and Jaccard index are commonly used metrics for the evaluation
of segmentation tasks in medical imaging. Convolutional neural networks trained
for image segmentation tasks are usually optimized for (weighted)
cross-entropy. This introduces an adverse discrepancy between the learning
optimization objective (the loss) and the end target metric. Recent works in
computer vision have proposed soft surrogates to alleviate this discrepancy and
directly optimize the desired metric, either through relaxations (soft-Dice,
soft-Jaccard) or submodular optimization (Lov\'asz-softmax). The aim of this
study is two-fold. First, we investigate the theoretical differences in a risk
minimization framework and question the existence of a weighted cross-entropy
loss with weights theoretically optimized to surrogate Dice or Jaccard. Second,
we empirically investigate the behavior of the aforementioned loss functions
w.r.t. evaluation with Dice score and Jaccard index on five medical
segmentation tasks. Through the application of relative approximation bounds,
we show that all surrogates are equivalent up to a multiplicative factor, and
that no optimal weighting of cross-entropy exists to approximate Dice or
Jaccard measures. We validate these findings empirically and show that, while
it is important to opt for one of the target metric surrogates rather than a
cross-entropy-based loss, the choice of the surrogate does not make a
statistical difference on a wide range of medical segmentation tasks.Comment: MICCAI 201
LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
Deep Learning (DL) models are becoming larger, because the increase in model
size might offer significant accuracy gain. To enable the training of large
deep networks, data parallelism and model parallelism are two well-known
approaches for parallel training. However, data parallelism does not help
reduce memory footprint per device. In this work, we introduce Large deep 3D
ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of
both input's and deep 3D ConvNets' size on segmentation accuracy. Through
automated model parallelism, it is feasible to train large deep 3D ConvNets
with a large input patch, even the whole image. Extensive experiments
demonstrate that, facilitated by the automated model parallelism, the
segmentation accuracy can be improved through increasing model size and input
context size, and large input yields significant inference speedup compared
with sliding window of small patches in the inference. Code is
available\footnote{https://monai.io/research/lamp-automated-model-parallelism}.Comment: MICCAI 2020 Early Accepted paper. Code is
available\footnote{https://monai.io/research/lamp-automated-model-parallelism
Presumed small vessel disease, imaging and cognition markers in the Alzheimer's Disease Neuroimaging Initiative
MRI-derived features of presumed cerebral small vessel disease are frequently found in Alzheimer’s disease. Influences of such markers on disease-progression measures are poorly understood. We measured markers of presumed small vessel disease (white matter hyperintensity volumes; cerebral microbleeds) on baseline images of newly enrolled individuals in the Alzheimer’s Disease Neuroimaging Initiative cohort (GO and 2) and used linear mixed models to relate these to subsequent atrophy and neuropsychological score change. We also assessed heterogeneity in white matter hyperintensity positioning within biomarker abnormality sequences, driven by the data, using the Subtype and Stage Inference algorithm. This study recruited both sexes and included: controls: [n = 159, mean(SD) age = 74(6) years]; early and late mild cognitive impairment [ns = 265 and 139, respectively, mean(SD) ages =71(7) and 72(8) years, respectively]; Alzheimer’s disease [n = 103, mean(SD) age = 75(8)] and significant memory concern [n = 72, mean(SD) age = 72(6) years]. Baseline demographic and vascular risk-factor data, and longitudinal cognitive scores (Mini-Mental State Examination; logical memory; and Trails A and B) were collected. Whole-brain and hippocampal volume change metrics were calculated. White matter hyperintensity volumes were associated with greater whole-brain and hippocampal volume changes independently of cerebral microbleeds (a doubling of baseline white matter hyperintensity was associated with an increase in atrophy rate of 0.3 ml/year for brain and 0.013 ml/year for hippocampus). Cerebral microbleeds were found in 15% of individuals and the presence of a microbleed, as opposed to none, was associated with increases in atrophy rate of 1.4 ml/year for whole brain and 0.021 ml/year for hippocampus. White matter hyperintensities were predictive of greater decline in all neuropsychological scores, while cerebral microbleeds were predictive of decline in logical memory (immediate recall) and Mini-Mental State Examination scores. We identified distinct groups with specific sequences of biomarker abnormality using continuous baseline measures and brain volume change. Four clusters were found; Group 1 showed early Alzheimer’s pathology; Group 2 showed early neurodegeneration; Group 3 had early mixed Alzheimer’s and cerebrovascular pathology; Group 4 had early neuropsychological score abnormalities. White matter hyperintensity volumes becoming abnormal was a late event for Groups 1 and 4 and an early event for 2 and 3. In summary, white matter hyperintensities and microbleeds were independently associated with progressive neurodegeneration (brain atrophy rates) and cognitive decline (change in neuropsychological scores). Mechanisms involving white matter hyperintensities and progression and microbleeds and progression may be partially separate. Distinct sequences of biomarker progression were found. White matter hyperintensity development was an early event in two sequences
Learning to segment when experts disagree
Recent years have seen an increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depend on the quality of labels, especially in medical image domain, where both the annotation cost and inter-observer variability are high. In a typical annotation collection process, different clinical experts provide their estimates of the “true” segmentation labels under the influence of their levels of expertise and biases. Treating these noisy labels blindly as the ground truth can adversely affect the performance of supervised segmentation models. In this work, we present a neural network architecture for jointly learning, from noisy observations alone, both the reliability of individual annotators and the true segmentation label distributions. The separation of the annotators’ characteristics and true segmentation label is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the training data. Our method can also be viewed as a translation of STAPLE, an established label aggregation framework proposed in Warfield et al. [1] to the supervised learning paradigm. We demonstrate first on a generic segmentation task using MNIST data and then adapt for usage with MRI scans of multiple sclerosis (MS) patients for lesion labelling. Our method shows considerable improvement over the relevant baselines on both datasets in terms of segmentation accuracy and estimation of annotator reliability, particularly when only a single label is available per image. An open-source implementation of our approach can be found at https://github.com/UCLBrain/MSLS
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels
Segmentation of the developing fetal brain is an important step in
quantitative analyses. However, manual segmentation is a very time-consuming
task which is prone to error and must be completed by highly specialized
indi-viduals. Super-resolution reconstruction of fetal MRI has become standard
for processing such data as it improves image quality and resolution. However,
dif-ferent pipelines result in slightly different outputs, further complicating
the gen-eralization of segmentation methods aiming to segment super-resolution
data. Therefore, we propose using transfer learning with noisy multi-class
labels to automatically segment high resolution fetal brain MRIs using a single
set of seg-mentations created with one reconstruction method and tested for
generalizability across other reconstruction methods. Our results show that the
network can auto-matically segment fetal brain reconstructions into 7 different
tissue types, regard-less of reconstruction method used. Transfer learning
offers some advantages when compared to training without pre-initialized
weights, but the network trained on clean labels had more accurate
segmentations overall. No additional manual segmentations were required.
Therefore, the proposed network has the potential to eliminate the need for
manual segmentations needed in quantitative analyses of the fetal brain
independent of reconstruction method used, offering an unbiased way to quantify
normal and pathological neurodevelopment.Comment: Accepted for publication at PIPPI MICCAI 202
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