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Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge [article]

Xiahai Zhuang, Jiahang Xu, Xinzhe Luo, Chen Chen, Cheng Ouyang, Daniel Rueckert, Victor M. Campello, Karim Lekadir, Sulaiman Vesal, Nishant RaviKumar, Yashu Liu, Gongning Luo (+11 others)
2021 arXiv   pre-print
This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019.  ...  Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI.  ...  Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) sequence can visualize MI, attributed to the slow washout kinetics of the gadolinium in the infarcted areas, which appear with distinctive  ... 
arXiv:2006.12434v2 fatcat:s4f3cchzhvhb3kyogwo3j5mmci

Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives [article]

Yinzhe Wu, Zeyu Tang, Binghuan Li, David Firmin, Guang Yang
2021 arXiv   pre-print
Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful for its efficacy in guiding the clinical diagnosis and treatment reliably.  ...  This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilising different modalities for accurate cardiac fibrosis and scar segmentation.  ...  AF 2018 LA segmentation challenge [64] MICCAI LGE MRI (150) LA cavity AF 2019 Multi-sequence Cardiac MR Segmentation Challenge (MS-CMR) [42] MICCAI LGE MRI, T2 MRI, bSSFP MRI (45  ... 
arXiv:2106.15707v1 fatcat:tk4bz7nux5bhhb3s7bv5z64squ

Segmenting Atrial Fibrosis from Late Gadolinium-Enhanced Cardiac MRI by Deep-Learned Features with Stacked Sparse Auto-Encoders [chapter]

Guang Yang, Xiahai Zhuang, Habib Khan, Shouvik Haldar, Eva Nyktari, Xujiong Ye, Greg Slabaugh, Tom Wong, Raad Mohiaddin, Jennifer Keegan, David Firmin
2017 Communications in Computer and Information Science  
The late gadolinium-enhanced (LGE) MRI technique is a wellvalidated method for fibrosis detection in the myocardium.  ...  This is followed by a supervised deep learning method for AFS. Twenty clinical LGE MRI scans from longstanding persistent AF patients were entered into this study retrospectively.  ...  Method Cardiac MRI Data Acquisition Cardiac MRI acquisitions were performed on a Siemens Magnetom Avanto 1.5T scanner.  ... 
doi:10.1007/978-3-319-60964-5_17 fatcat:3e2nk6vsdrhgrgtqwylbrq7a2i

A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images

Guang Yang, Xiahai Zhuang, Habib Khan, Shouvik Haldar, Eva Nyktari, Xujiong Ye, Greg Slabaugh, Tom Wong, Raad Mohiaddin, Jennifer Keegan, David Firmin
2017 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)  
Precise and objective segmentation of atrial scarring (SAS) is a prerequisite for quantitative assessment of atrial fibrillation using non-invasive late gadolinium-enhanced (LGE) MRI.  ...  We demonstrate the efficacy of our method on 20 clinical LGE MRI scans acquired from a longstanding persistent atrial fibrillation cohort.  ...  Cardiac MR data were acquired on a Siemens Magnetom Avanto 1.5T scanner.  ... 
doi:10.1109/isbi.2017.7950649 dblp:conf/isbi/YangZKHNYSWMKF17 fatcat:fabxedl6vrf6pmw5xshaan737q

Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images

Guang Yang, Jun Chen, Zhifan Gao, Heye Zhang, Hao Ni, Elsa Angelini, Raad Mohiaddin, Tom Wong, Jennifer Keegan, David Firmin
2018 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution  ...  In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically  ...  Veins from Late Gadolinium-Enhanced Cardiac MRI Images * of the imaged organs in medical images, which is analogous to the active contour model, but also learns patterns of shape variability from a training  ... 
doi:10.1109/embc.2018.8512550 pmid:30440587 fatcat:hihm4h4otfbznaa7kxge54is3i

Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI

Guang Yang, Xiahai Zhuang, Habib Khan, Shouvik Haldar, Eva Nyktari, Lei Li, Ricardo Wage, Xujiong Ye, Greg Slabaugh, Raad Mohiaddin, Tom Wong, Jennifer Keegan (+1 others)
2018 Medical Physics (Lancaster)  
It is associated with atrial fibrosis, which may be assessed non-invasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualised as a region of signal enhancement  ...  Methods: Our fully automatic pipeline uniquely combined: (1) a multi-atlas based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from an MRI Roadmap acquisition which is then mapped  ...  Segmentation of the atrial scarring from LGE MRI images is a very challenging problem.  ... 
doi:10.1002/mp.12832 pmid:29480931 pmcid:PMC5969251 fatcat:adpi4ouwbveh5dghpkeidoqory

Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review [article]

Lei Li and Veronika A. Zimmer and Julia A. Schnabel and Xiahai Zhuang
2022 arXiv   pre-print
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.  ...  Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars.  ...  JA Schnabel and VA Zimmer would like to acknowledge funding from a Wellcome Trust IEH Award (WT 102431), an EPSRC programme grant (EP/P001009/1), and the Wellcome/EPSRC Center for Medical Engineering (  ... 
arXiv:2106.09862v3 fatcat:y7gk5bjqirgotbx3bwfq62rnqy

Multi-Modality Cardiac Image Analysis with Deep Learning [article]

Lei Li, Fuping Wu, Sihang Wang, Xiahai Zhuang
2021 arXiv   pre-print
Firstly, we introduce two benchmark works for multi-sequence cardiac MRI based myocardial and pathology segmentation.  ...  Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is a promising technique to visualize and quantify myocardial infarction (MI) and atrial scars.  ...  Methodology summary for challenge events MS-CMRSeg Challenge: Cardiac Segmentation on Late Gadolinium Enhancement MRI We organized multi-sequence cardiac MR segmentation (MS-CMRSeg) challenge, in conjunction  ... 
arXiv:2111.04736v1 fatcat:pdxoa7p23jhknc7rvtdydurqma

Atrial Fibrosis Quantification Based on Maximum Likelihood Estimator of Multivariate Images [chapter]

Fuping Wu, Lei Li, Guang Yang, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Lingchao Xu, Xiahai Zhuang
2018 Lecture Notes in Computer Science  
We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other  ...  is an anatomical MRI from the same acquisition session.  ...  solely based on single-sequence cardiac MRI.  ... 
doi:10.1007/978-3-030-00937-3_69 fatcat:flcnhnlbpvhcnjeuzjcdwa5ajm

Multi-sequence CMR based myocardial pathology segmentation challenge [article]

Xiahai Zhuang, Lei Li
2020 Zenodo  
Cardiac magnetic resonance (CMR) is particularly used to provide imaging anatomical and functional information of heart, such as the late gadolinium enhancement (LGE) CMR sequence which visualizes MI,  ...  This is the challenge design document for the "Multi-sequence CMR based myocardial pathology segmentation challenge", accepted for MICCAI 2020.  ...  The data providing platform/source is same as our last challenge, i.e., Multi-sequence Cardiac MR Segmentation Challenge 2019.  ... 
doi:10.5281/zenodo.3715932 fatcat:ywbeb3yasbajpiz6jq6o6qfhzy

An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks.  ...  As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI.  ...  They can also be used to reduce the gadolinium dose in contrast-enhanced brain MRI by an order of magnitude [87] without significant reduction in image quality.  ... 
doi:10.1016/j.zemedi.2018.11.002 fatcat:kkimovnwcrhmth7mg6h6cpomjm

Deep learning for cardiac image segmentation: A review [article]

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2019 arXiv   pre-print
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  ...  Deep learning has become the most widely used approach for cardiac image segmentation in recent years.  ...  By utilizing different imaging sequences, cardiac MRI allows accurate quantification of both cardiac anatomy and function (e.g. cine imaging) and pathological tissues such as scars (late gadolinium enhancement  ... 
arXiv:1911.03723v1 fatcat:cwsq5hiaebgkza5ktmtyw553je

Deep Learning for Cardiac Image Segmentation: A Review

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2020 Frontiers in Cardiovascular Medicine  
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  ...  Deep learning has become the most widely used approach for cardiac image segmentation in recent years.  ...  colleagues: Karl Hahn, Qingjie Meng, James Batten, and Jonathan Passerat-Palmbach who provided the insight and expertise that greatly assisted the work, and also constructive and thoughtful comments from  ... 
doi:10.3389/fcvm.2020.00025 pmid:32195270 pmcid:PMC7066212 fatcat:iw7xpnltn5cgbn5ullq2ldy3nq

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images [article]

Lei Li, Fuping Wu, Sihan Wang, Xinzhe Luo, Carlos Martin-Isla, Shuwei Zhai, Jianpeng Zhang, Yanfei Liu7, Zhen Zhang, Markus J. Ankenbrand, Haochuan Jiang, Xiaoran Zhang (+20 others)
2022 arXiv   pre-print
This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed  ...  The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation.  ...  I2CVB provided a multi-parametric MR image dataset, including T2 MR, dynamic contrast enhanced (DCE) MR, DWI MR and MR spectroscopic imaging data, and was aimed for prostate cancer segmentation (Vall  ... 
arXiv:2201.03186v1 fatcat:u7e5yqn2evdrxacoboz5q63eym

Synthetic Generation of Myocardial Blood–Oxygen-Level-Dependent MRI Time Series Via Structural Sparse Decomposition Modeling

Cristian Rusu, Rita Morisi, Davide Boschetto, Rohan Dharmakumar, Sotirios A. Tsaftaris
2014 IEEE Transactions on Medical Imaging  
We do this by 1) modeling the temporal changes in BOLD signal intensity based on sparse multi-component dictionary learning, whereby segmentally derived myocardial time series are extracted from canine  ...  CP-BOLD MRI is a new contrast agent-and stress-free approach for examining changes in myocardial oxygenation in response to coronary artery disease.  ...  Late gadolinium enhancement (LGE) images were acquired within 20 min post-occlusion (to rule out early infarction) and after 3 h of occlusion and during reperfusion (to identify myocardial regions succumbed  ... 
doi:10.1109/tmi.2014.2313000 pmid:24691119 pmcid:PMC4079741 fatcat:hmvnbrsslfdcrfs3kfrwfo2yd4
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