231 research outputs found
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
Deep-learning has proved in recent years to be a powerful tool for image
analysis and is now widely used to segment both 2D and 3D medical images.
Deep-learning segmentation frameworks rely not only on the choice of network
architecture but also on the choice of loss function. When the segmentation
process targets rare observations, a severe class imbalance is likely to occur
between candidate labels, thus resulting in sub-optimal performance. In order
to mitigate this issue, strategies such as the weighted cross-entropy function,
the sensitivity function or the Dice loss function, have been proposed. In this
work, we investigate the behavior of these loss functions and their sensitivity
to learning rate tuning in the presence of different rates of label imbalance
across 2D and 3D segmentation tasks. We also propose to use the class
re-balancing properties of the Generalized Dice overlap, a known metric for
segmentation assessment, as a robust and accurate deep-learning loss function
for unbalanced tasks
A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality
Quality control (QC) of medical images is essential to ensure that downstream
analyses such as segmentation can be performed successfully. Currently, QC is
predominantly performed visually at significant time and operator cost. We aim
to automate the process by formulating a probabilistic network that estimates
uncertainty through a heteroscedastic noise model, hence providing a proxy
measure of task-specific image quality that is learnt directly from the data.
By augmenting the training data with different types of simulated k-space
artefacts, we propose a novel cascading CNN architecture based on a
student-teacher framework to decouple sources of uncertainty related to
different k-space augmentations in an entirely self-supervised manner. This
enables us to predict separate uncertainty quantities for the different types
of data degradation. While the uncertainty measures reflect the presence and
severity of image artefacts, the network also provides the segmentation
predictions given the quality of the data. We show models trained with
simulated artefacts provide informative measures of uncertainty on real-world
images and we validate our uncertainty predictions on problematic images
identified by human-raters
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the \textit{VAscular Lesions DetectiOn and Segmentation} (\textit{Where is VALDO?}) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level
Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound
© 2020, Springer Nature Switzerland AG. Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81 and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Feature attribution (FA), or the assignment of class-relevance to different
locations in an image, is important for many classification problems but is
particularly crucial within the neuroscience domain, where accurate mechanistic
models of behaviours, or disease, require knowledge of all features
discriminative of a trait. At the same time, predicting class relevance from
brain images is challenging as phenotypes are typically heterogeneous, and
changes occur against a background of significant natural variation. Here, we
present a novel framework for creating class specific FA maps through
image-to-image translation. We propose the use of a VAE-GAN to explicitly
disentangle class relevance from background features for improved
interpretability properties, which results in meaningful FA maps. We validate
our method on 2D and 3D brain image datasets of dementia (ADNI dataset), ageing
(UK Biobank), and (simulated) lesion detection. We show that FA maps generated
by our method outperform baseline FA methods when validated against ground
truth. More significantly, our approach is the first to use latent space
sampling to support exploration of phenotype variation. Our code will be
available online at https://github.com/CherBass/ICAM.Comment: Submitted to NeurIPS 2020: Neural Information Processing Systems.
Keywords: interpretable, classification, feature attribution, domain
translation, variational autoencoder, generative adversarial network,
neuroimagin
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
APOE ?4 status is associated with white matter hyperintensities volume accumulation rate independent of AD diagnosis.
To assess the relationship between carriage of APOE ?4 allele and evolution of white matter hyperintensities (WMHs) volume, we longitudinally studied 339 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort with diagnoses ranging from normal controls to probable Alzheimer's disease (AD). A purpose-built longitudinal automatic method was used to segment WMH using constraints derived from an atlas-based model selection applied to a time-averaged image. Linear mixed models were used to evaluate the differences in rate of change across diagnosis and genetic groups. After adjustment for covariates (age, sex, and total intracranial volume), homozygous APOE ?4?4 subjects had a significantly higher rate of WMH accumulation (22.5% per year 95% CI [14.4, 31.2] for a standardized population having typical values of covariates) compared with the heterozygous (?4?3) subjects (10.0% per year [6.7, 13.4]) and homozygous ?3?3 (6.6% per year [4.1, 9.3]) subjects. Rates of accumulation increased with diagnostic severity; controls accumulated 5.8% per year 95% CI: [2.2, 9.6] for the standardized population, early mild cognitive impairment 6.6% per year [3.9, 9.4], late mild cognitive impairment 12.5% per year [8.2, 17.0] and AD subjects 14.7% per year [6.0, 24.0]. Following adjustment for APOE status, these differences became nonstatistically significant suggesting that APOE ?4 genotype is the major driver of accumulation of WMH volume rather than diagnosis of AD
NiftyNet: a deep-learning platform for medical imaging
Medical image analysis and computer-assisted intervention problems are
increasingly being addressed with deep-learning-based solutions. Established
deep-learning platforms are flexible but do not provide specific functionality
for medical image analysis and adapting them for this application requires
substantial implementation effort. Thus, there has been substantial duplication
of effort and incompatible infrastructure developed across many research
groups. This work presents the open-source NiftyNet platform for deep learning
in medical imaging. The ambition of NiftyNet is to accelerate and simplify the
development of these solutions, and to provide a common mechanism for
disseminating research outputs for the community to use, adapt and build upon.
NiftyNet provides a modular deep-learning pipeline for a range of medical
imaging applications including segmentation, regression, image generation and
representation learning applications. Components of the NiftyNet pipeline
including data loading, data augmentation, network architectures, loss
functions and evaluation metrics are tailored to, and take advantage of, the
idiosyncracies of medical image analysis and computer-assisted intervention.
NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D
and 3D images and computational graphs by default.
We present 3 illustrative medical image analysis applications built using
NiftyNet: (1) segmentation of multiple abdominal organs from computed
tomography; (2) image regression to predict computed tomography attenuation
maps from brain magnetic resonance images; and (3) generation of simulated
ultrasound images for specified anatomical poses.
NiftyNet enables researchers to rapidly develop and distribute deep learning
solutions for segmentation, regression, image generation and representation
learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge
Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6
figures; Update includes additional applications, updated author list and
formatting for journal submissio
Assessment of Microvascular Disease in Heart and Brain by MRI: Application in Heart Failure with Preserved Ejection Fraction and Cerebral Small Vessel Disease
The objective of this review is to investigate the commonalities of microvascular (small vessel) disease in heart failure with preserved ejection fraction (HFpEF) and cerebral small vessel disease (CSVD). Furthermore, the review aims to evaluate the current magnetic resonance imaging (MRI) diagnostic techniques for both conditions. By comparing the two conditions, this review seeks to identify potential opportunities to improve the understanding of both HFpEF and CSVD
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