510 research outputs found
Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI
Congenital Heart Disease (CHD) is a group of cardiac malformations present
already during fetal life, representing the prevailing category of birth
defects globally. Our aim in this study is to aid 3D fetal vessel topology
visualisation in aortic arch anomalies, a group which encompasses a range of
conditions with significant anatomical heterogeneity. We present a multi-task
framework for automated multi-class fetal vessel segmentation from 3D black
blood T2w MRI and anomaly classification. Our training data consists of binary
manual segmentation masks of the cardiac vessels' region in individual subjects
and fully-labelled anomaly-specific population atlases. Our framework combines
deep learning label propagation using VoxelMorph with 3D Attention U-Net
segmentation and DenseNet121 anomaly classification. We target 11 cardiac
vessels and three distinct aortic arch anomalies, including double aortic arch,
right aortic arch, and suspected coarctation of the aorta. We incorporate an
anomaly classifier into our segmentation pipeline, delivering a multi-task
framework with the primary motivation of correcting topological inaccuracies of
the segmentation. The hypothesis is that the multi-task approach will encourage
the segmenter network to learn anomaly-specific features. As a secondary
motivation, an automated diagnosis tool may have the potential to enhance
diagnostic confidence in a decision support setting. Our results showcase that
our proposed training strategy significantly outperforms label propagation and
a network trained exclusively on propagated labels. Our classifier outperforms
a classifier trained exclusively on T2w volume images, with an average balanced
accuracy of 0.99 (0.01) after joint training. Adding a classifier improves the
anatomical and topological accuracy of all correctly classified double aortic
arch subjects.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://melba-journal.org/2023:01
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
In recent years, cardiovascular diseases (CVDs) have become one of the
leading causes of mortality globally. CVDs appear with minor symptoms and
progressively get worse. The majority of people experience symptoms such as
exhaustion, shortness of breath, ankle swelling, fluid retention, and other
symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia,
cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina
are the most common CVDs. Clinical methods such as blood tests,
electrocardiography (ECG) signals, and medical imaging are the most effective
methods used for the detection of CVDs. Among the diagnostic methods, cardiac
magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the
disease, plan treatment and predict CVDs. Coupled with all the advantages of
CMR data, CVDs diagnosis is challenging for physicians due to many slices of
data, low contrast, etc. To address these issues, deep learning (DL) techniques
have been employed to the diagnosis of CVDs using CMR data, and much research
is currently being conducted in this field. This review provides an overview of
the studies performed in CVDs detection using CMR images and DL techniques. The
introduction section examined CVDs types, diagnostic methods, and the most
important medical imaging techniques. In the following, investigations to
detect CVDs using CMR images and the most significant DL methods are presented.
Another section discussed the challenges in diagnosing CVDs from CMR data.
Next, the discussion section discusses the results of this review, and future
work in CVDs diagnosis from CMR images and DL techniques are outlined. The most
important findings of this study are presented in the conclusion section
Faster 3D cardiac CT segmentation with Vision Transformers
Accurate segmentation of the heart is essential for personalized blood flow
simulations and surgical intervention planning. A recent advancement in image
recognition is the Vision Transformer (ViT), which expands the field of view to
encompass a greater portion of the global image context. We adapted ViT for
three-dimensional volume inputs. Cardiac computed tomography (CT) volumes from
39 patients, featuring up to 20 timepoints representing the complete cardiac
cycle, were utilized. Our network incorporates a modified ResNet50 block as
well as a ViT block and employs cascade upsampling with skip connections.
Despite its increased model complexity, our hybrid Transformer-Residual U-Net
framework, termed TRUNet, converges in significantly less time than residual
U-Net while providing comparable or superior segmentations of the left
ventricle, left atrium, left atrial appendage, ascending aorta, and pulmonary
veins. TRUNet offers more precise vessel boundary segmentation and better
captures the heart's overall anatomical structure compared to residual U-Net,
as confirmed by the absence of extraneous clusters of missegmented voxels. In
terms of both performance and training speed, TRUNet exceeded U-Net, a commonly
used segmentation architecture, making it a promising tool for 3D semantic
segmentation tasks in medical imaging. The code for TRUNet is available at
github.com/ljollans/TRUNet
Multi-modality cardiac image computing: a survey
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities.
This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future
Binary segmentation of medical images using implicit spline representations and deep learning
We propose a novel approach to image segmentation based on combining implicit
spline representations with deep convolutional neural networks. This is done by
predicting the control points of a bivariate spline function whose zero-set
represents the segmentation boundary. We adapt several existing neural network
architectures and design novel loss functions that are tailored towards
providing implicit spline curve approximations. The method is evaluated on a
congenital heart disease computed tomography medical imaging dataset.
Experiments are carried out by measuring performance in various standard
metrics for different networks and loss functions. We determine that splines of
bidegree with coefficient resolution performed optimally
for resolution CT images. For our best network, we achieve an
average volumetric test Dice score of almost 92%, which reaches the state of
the art for this congenital heart disease dataset.Comment: 17 pages, 5 figure
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