36 research outputs found
Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-sequence Cardiac MR Images Segmentation
Analysis and modeling of the ventricles and myocardium are important in the
diagnostic and treatment of heart diseases. Manual delineation of those tissues
in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the
boundaries makes the segmentation task rather challenging. Furthermore, the
annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI,
are often not available. We propose an end-to-end segmentation framework based
on convolutional neural network (CNN) and adversarial learning. A dilated
residual U-shape network is used as a segmentor to generate the prediction
mask; meanwhile, a CNN is utilized as a discriminator model to judge the
segmentation quality. To leverage the available annotations across modalities
per patient, a new loss function named weak domain-transfer loss is introduced
to the pipeline. The proposed model is evaluated on the public dataset released
by the challenge organizer in MICCAI 2019, which consists of 45 sets of
multi-sequence CMR images. We demonstrate that the proposed adversarial
pipeline outperforms baseline deep-learning methods.Comment: 9 pages, 4 figures, conferenc
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
In cardiac magnetic resonance imaging, fully-automatic segmentation of the
heart enables precise structural and functional measurements to be taken, e.g.
from short-axis MR images of the left-ventricle. In this work we propose a
recurrent fully-convolutional network (RFCN) that learns image representations
from the full stack of 2D slices and has the ability to leverage inter-slice
spatial dependences through internal memory units. RFCN combines anatomical
detection and segmentation into a single architecture that is trained
end-to-end thus significantly reducing computational time, simplifying the
segmentation pipeline, and potentially enabling real-time applications. We
report on an investigation of RFCN using two datasets, including the publicly
available MICCAI 2009 Challenge dataset. Comparisons have been carried out
between fully convolutional networks and deep restricted Boltzmann machines,
including a recurrent version that leverages inter-slice spatial correlation.
Our studies suggest that RFCN produces state-of-the-art results and can
substantially improve the delineation of contours near the apex of the heart.Comment: MICCAI Workshop RAMBO 201
A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI
With respect to spatial overlap, CNN-based segmentation of short axis
cardiovascular magnetic resonance (CMR) images has achieved a level of
performance consistent with inter observer variation. However, conventional
training procedures frequently depend on pixel-wise loss functions, limiting
optimisation with respect to extended or global features. As a result, inferred
segmentations can lack spatial coherence, including spurious connected
components or holes. Such results are implausible, violating the anticipated
topology of image segments, which is frequently known a priori. Addressing this
challenge, published work has employed persistent homology, constructing
topological loss functions for the evaluation of image segments against an
explicit prior. Building a richer description of segmentation topology by
considering all possible labels and label pairs, we extend these losses to the
task of multi-class segmentation. These topological priors allow us to resolve
all topological errors in a subset of 150 examples from the ACDC short axis CMR
training data set, without sacrificing overlap performance.Comment: To be presented at the STACOM workshop at MICCAI 202
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Background: Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images.
Methods: Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV).
Results: By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement was 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric was 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability.
Conclusions: We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures
Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results
Funding was provided
by British Heart Foundation (PG/14/89/31194), and by the National
Institutes of Health (USA) 1R01HL121754. SN, SKP acknowledge
the National Institute for Health Research (NIHR) Oxford Biomedical
Research Centre based at The Oxford University Hospitals Trust
at the University of Oxford, and the British Heart Foundation Centre
of Research Excellence. Aaron Lee and Steffen Petersen acknowledge
support from the NIHR Biomedical Research Centre at Barts Health
NHS Trust and from the “SmartHeart” EPSRC programme grant (EP/
P001009/1)