572 research outputs found
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection
Quality assessment of medical images is essential for complete automation of
image processing pipelines. For large population studies such as the UK
Biobank, artefacts such as those caused by heart motion are problematic and
manual identification is tedious and time-consuming. Therefore, there is an
urgent need for automatic image quality assessment techniques. In this paper,
we propose a method to automatically detect the presence of motion-related
artefacts in cardiac magnetic resonance (CMR) images. As this is a highly
imbalanced classification problem (due to the high number of good quality
images compared to the low number of images with motion artefacts), we propose
a novel k-space based training data augmentation approach in order to address
this problem. Our method is based on 3D spatio-temporal Convolutional Neural
Networks, and is able to detect 2D+time short axis images with motion artefacts
in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset
consisting of 3465 CMR images and achieve not only high accuracy in detection
of motion artefacts, but also high precision and recall. We compare our
approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
Deep learning approaches have achieved state-of-the-art performance in
cardiac magnetic resonance (CMR) image segmentation. However, most approaches
have focused on learning image intensity features for segmentation, whereas the
incorporation of anatomical shape priors has received less attention. In this
paper, we combine a multi-task deep learning approach with atlas propagation to
develop a shape-constrained bi-ventricular segmentation pipeline for short-axis
CMR volumetric images. The pipeline first employs a fully convolutional network
(FCN) that learns segmentation and landmark localisation tasks simultaneously.
The architecture of the proposed FCN uses a 2.5D representation, thus combining
the computational advantage of 2D FCNs networks and the capability of
addressing 3D spatial consistency without compromising segmentation accuracy.
Moreover, the refinement step is designed to explicitly enforce a shape
constraint and improve segmentation quality. This step is effective for
overcoming image artefacts (e.g. due to different breath-hold positions and
large slice thickness), which preclude the creation of anatomically meaningful
3D cardiac shapes. The proposed pipeline is fully automated, due to network's
ability to infer landmarks, which are then used downstream in the pipeline to
initialise atlas propagation. We validate the pipeline on 1831 healthy subjects
and 649 subjects with pulmonary hypertension. Extensive numerical experiments
on the two datasets demonstrate that our proposed method is robust and capable
of producing accurate, high-resolution and anatomically smooth bi-ventricular
3D models, despite the artefacts in input CMR volumes
Automatic Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment using Unsupervised Domain Adaptation in Spatial and Frequency Domains
Population imaging studies rely upon good quality medical imagery before
downstream image quantification. This study provides an automated approach to
assess image quality from cardiovascular magnetic resonance (CMR) imaging at
scale. We identify four common CMR imaging artefacts, including respiratory
motion, cardiac motion, Gibbs ringing, and aliasing. The model can deal with
images acquired in different views, including two, three, and four-chamber
long-axis and short-axis cine CMR images. Two deep learning-based models in
spatial and frequency domains are proposed. Besides recognising these
artefacts, the proposed models are suitable to the common challenges of not
having access to data labels. An unsupervised domain adaptation method and a
Fourier-based convolutional neural network are proposed to overcome these
challenges. We show that the proposed models reliably allow for CMR image
quality assessment. The accuracies obtained for the spatial model in supervised
and weakly supervised learning are 99.41+0.24 and 96.37+0.66 for the UK Biobank
dataset, respectively. Using unsupervised domain adaptation can somewhat
overcome the challenge of not having access to the data labels. The maximum
achieved domain gap coverage in unsupervised domain adaptation is 16.86%.
Domain adaptation can significantly improve a 5-class classification task and
deal with considerable domain shift without data labels. Increasing the speed
of training and testing can be achieved with the proposed model in the
frequency domain. The frequency-domain model can achieve the same accuracy yet
1.548 times faster than the spatial model. This model can also be used directly
on k-space data, and there is no need for image reconstruction.Comment: 21 pages, 9 figures, 7 table
MRI Artefact Augmentation: Robust Deep Learning Systems and Automated Quality Control
Quality control (QC) of magnetic resonance imaging (MRI) is essential to establish whether a scan or dataset meets a required set of standards. In MRI, many potential artefacts must be identified so that problematic images can either be excluded or accounted for in further image processing or analysis. To date, the gold standard for the identification of these issues is visual inspection by experts.
A primary source of MRI artefacts is caused by patient movement, which can affect clinical diagnosis and impact the accuracy of Deep Learning systems. In this thesis, I present a method to simulate motion artefacts from artefact-free images to augment convolutional neural networks (CNNs), increasing training appearance variability and robustness to motion artefacts. I show that models trained with artefact augmentation generalise better and are more robust to real-world artefacts, with negligible cost to performance on clean data. I argue that it is often better to optimise frameworks end-to-end with artefact augmentation rather than learning to retrospectively remove artefacts, thus enforcing robustness to artefacts at the feature level representation of the data.
The labour-intensive and subjective nature of QC has increased interest in automated methods. To address this, I approach MRI quality estimation as the uncertainty in performing a downstream task, using probabilistic CNNs to predict segmentation uncertainty as a function of the input data. Extending this framework, I introduce a novel decoupled uncertainty model, enabling separate uncertainty predictions for different types of image degradation. Training with an extended k-space artefact augmentation pipeline, the model provides informative measures of uncertainty on problematic real-world scans classified by QC raters and enables sources of segmentation uncertainty to be identified.
Suitable quality for algorithmic processing may differ from an image's perceptual quality. Exploring this, I pose MRI visual quality assessment as an image restoration task. Using Bayesian CNNs to recover clean images from noisy data, I show that the uncertainty indicates the possible recoverability of an image. A multi-task network combining uncertainty-aware artefact recovery with tissue segmentation highlights the distinction between visual and algorithmic quality, which has the impact that, depending on the downstream task, less data should be discarded for purely visual quality reasons
A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance Images
Background and Objectives: Cardiovascular magnetic resonance (CMR) imaging is
a powerful modality in functional and anatomical assessment for various
cardiovascular diseases. Sufficient image quality is essential to achieve
proper diagnosis and treatment. A large number of medical images, the variety
of imaging artefacts, and the workload of imaging centres are among the things
that reveal the necessity of automatic image quality assessment (IQA). However,
automated IQA requires access to bulk annotated datasets for training deep
learning (DL) models. Labelling medical images is a tedious, costly and
time-consuming process, which creates a fundamental challenge in proposing
DL-based methods for medical applications. This study aims to present a new
method for CMR IQA when there is limited access to annotated datasets. Methods:
The proposed generalised deep meta-learning model can evaluate the quality by
learning tasks in the prior stage and then fine-tuning the resulting model on a
small labelled dataset of the desired tasks. This model was evaluated on the
data of over 6,000 subjects from the UK Biobank for five defined tasks,
including detecting respiratory motion, cardiac motion, Aliasing and Gibbs
ringing artefacts and images without artefacts. Results: The results of
extensive experiments show the superiority of the proposed model. Besides,
comparing the model's accuracy with the domain adaptation model indicates a
significant difference by using only 64 annotated images related to the desired
tasks. Conclusion: The proposed model can identify unknown artefacts in images
with acceptable accuracy, which makes it suitable for medical applications and
quality assessment of large cohorts.Comment: 16 pages, 1 figure, 2 table
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