881 research outputs found
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Evaluation with an Independent Dataset of a Deep Learning-based Left Atrium Segmentation Method
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Director/s: Gaspar Delso i Roser Sala. Tutor: Manel PuigAtrial fibrillation (AF) is the most prevalent type of arrhythmia nowadays. Even though it is
associated with significant morbidity and mortality, there is still a substantial lack of basic
understanding of the left atrium (LA) and pulmonary veins (PVs) anatomical structure that curbs
the performance of current clinical treatments for the disease. Thus, segmentation and 3D
reconstruction of the LA and PVs are of crucial importance for the diagnosis and treatment of AF.
In this context, cardiac 3D Late Gadolinium Magnetic Resonance Imaging (LGE-MRI) appear as a
very good tool for cardiac tissue characterization and myocardial fibrosis detection. In fact, these
images have been proofed as reliable predictors of catheter ablation success, which is often the
chosen treatment for AF patients.
Several manual and semi-automatic segmentation tools from LGE-MRI scans are currently in use,
but these are very time-consuming and highly prone to errors, hence the need for an automatic
segmentation approach.
With the rise of deep learning and convolutional neural networks, a number of automatic schemes
are being developed. In this project, we evaluate a model that has been developed at the Hospital
Clínic de Barcelona for obtaining an automatic segmentation of the LA using a deep learning
architecture. Concretely, we tested this model with an independent set of images from another MRI
vendor, and we obtained a set of quantitative and qualitative measures to validate the results.
For the pursuit of our aims, this work begins with the state-of-the-art for LA segmentation of LGEMRI
scans and with a market analysis of the field. We then present our proposed solution together
with the obtained results and the corresponding conclusions
Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly
used to visualize and quantify left atrial (LA) scars. The position and extent
of scars provide important information of the pathophysiology and progression
of atrial fibrillation (AF). Hence, LA scar segmentation and quantification
from LGE MRI can be useful in computer-assisted diagnosis and treatment
stratification of AF patients. Since manual delineation can be time-consuming
and subject to intra- and inter-expert variability, automating this computing
is highly desired, which nevertheless is still challenging and
under-researched.
This paper aims to provide a systematic review on computing methods for LA
cavity, wall, scar and ablation gap segmentation and quantification from LGE
MRI, and the related literature for AF studies. Specifically, we first
summarize AF-related imaging techniques, particularly LGE MRI. Then, we review
the methodologies of the four computing tasks in detail, and summarize the
validation strategies applied in each task. Finally, the possible future
developments are outlined, with a brief survey on the potential clinical
applications of the aforementioned methods. The review shows that the research
into this topic is still in early stages. Although several methods have been
proposed, especially for LA segmentation, there is still large scope for
further algorithmic developments due to performance issues related to the high
variability of enhancement appearance and differences in image acquisition.Comment: 23 page
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Deep Learning-based Prescription of Cardiac MRI Planes.
PurposeTo develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)-based localization of key anatomic landmarks.Materials and methodsAnnotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation.ResultsOn LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, -1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively.ConclusionDL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.© RSNA, 2019 Supplemental material is available for this article
Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images
Accurate assessment of left atrial fibrosis in patients with atrial
fibrillation relies on high-quality 3D late gadolinium enhancement (LGE) MRI
images. However, obtaining such images is challenging due to patient motion,
changing breathing patterns, or sub-optimal choice of pulse sequence
parameters. Automated assessment of LGE-MRI image diagnostic quality is
clinically significant as it would enhance diagnostic accuracy, improve
efficiency, ensure standardization, and contributes to better patient outcomes
by providing reliable and high-quality LGE-MRI scans for fibrosis
quantification and treatment planning. To address this, we propose a two-stage
deep-learning approach for automated LGE-MRI image diagnostic quality
assessment. The method includes a left atrium detector to focus on relevant
regions and a deep network to evaluate diagnostic quality. We explore two
training strategies, multi-task learning, and pretraining using contrastive
learning, to overcome limited annotated data in medical imaging. Contrastive
Learning result shows about , and improvement in F1-Score and
Specificity compared to Multi-Task learning when there's limited data.Comment: Accepted to STACOM 2023. 11 pages, 3 figure
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