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

Yang, Guang; Zhuang, Xiahai; Khan, Habib; Haldar, Shouvik; Nyktari, Eva; Ye, Xujiong; Slabaugh, Greg; Wong, Tom; Mohiaddin, Raad H.; Keegan, Jennifer; Firmin, David

doi:10.1109/isbi.2017.7950649

Cited by 14 publications

(8 citation statements)

References 34 publications

(49 reference statements)

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“…SSAE is a deep neural network composed of multiple stacked sparse auto-encoders (SAEs) [37], and it has been applied in tissue segmentation in late gadolinium-enhanced cardiac MRI images (such as atrial scarring segmentation [38], atrial fibrosis segmentation [39], left atrium segmentation [40]), nuclei patch classification on breast cancer histopathology images [41], brain tissue segmentation in visible human images [42], or other applications (such as hyperspectral imagery classification [43] and building extraction from LiDAR and optical images [44]). Figure 3 shows a SSAE network with three hidden layers, where a SAE aims to learn features that form a good sparse representation of its input.…”

Section: Proposed Methodsmentioning

confidence: 99%

Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor

Niu

Lan²,

Wang³

2019

Sensors

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Automatic detection of left ventricle myocardium is essential to subsequent cardiac image registration and tissue segmentation. However, it is considered challenging mainly because of the complex and varying shape of the myocardium and surrounding tissues across slices and phases. In this study, a hybrid model is proposed to detect myocardium in cardiac magnetic resonance (MR) images combining region proposal and deep feature classification and regression. The model firstly generates candidate regions using new structural similarity-enhanced supervoxel over-segmentation plus hierarchical clustering. Then it adopts a deep stacked sparse autoencoder (SSAE) network to learn the discriminative deep feature to represent the regions. Finally, the features are fed to train a novel nonlinear within-class neighborhood preserved soft margin support vector (C-SVC) classifier and multiple-output support vector ( ε -SVR) regressor for refining the location of myocardium. To improve the stability and generalization, the model also takes hard negative sample mining strategy to fine-tune the SSAE and the classifier. The proposed model with impacts of different components were extensively evaluated and compared to related methods on public cardiac data set. Experimental results verified the effectiveness of proposed integrated components, and demonstrated that it was robust in myocardium localization and outperformed the state-of-the-art methods in terms of typical metrics. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.

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Section: Proposed Methodsmentioning

confidence: 99%

Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor

Niu

Lan²,

Wang³

2019

Sensors

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“…However, currently no deep learning approaches have been proposed for direct LA wall segmentation from LGE-MRI. Yang et al (85) proposed a hybrid approach combining multi-atlases and an unsupervised sparse auto-encoders for LA scar segmentation. A multi-atlas algorithm was used to segment the LA blood pool from the LGE-MRIs.…”

Section: Atrial Wall and Scar Segmentationmentioning

confidence: 99%

Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs

Jamart

Xiong

Talou

et al. 2020

Front. Cardiovasc. Med.

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Segmentation and 3D reconstruction of the human atria is of crucial importance for precise diagnosis and treatment of atrial fibrillation, the most common cardiac arrhythmia. However, the current manual segmentation of the atria from medical images is a time-consuming, labor-intensive, and error-prone process. The recent emergence of artificial intelligence, particularly deep learning, provides an alternative solution to the traditional methods that fail to accurately segment atrial structures from clinical images. This has been illustrated during the recent 2018 Atrial Segmentation Challenge for which most of the challengers developed deep learning approaches for atrial segmentation, reaching high accuracy (>90% Dice score). However, as significant discrepancies exist between the approaches developed, many important questions remain unanswered, such as which deep learning architectures and methods to ensure reliability while achieving the best performance. In this paper, we conduct an in-depth review of the current state-of-the-art of deep learning approaches for atrial segmentation from late gadolinium-enhanced MRIs, and provide critical insights for overcoming the main hindrances faced in this task.

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“…LGE CMR (enhanced regions) and electro-anatomical mapping systems (low voltage regions) [20] [21] used during an electrophysiology procedure. Yang et al [4] [22] proposed a supervised learning based method (using Support Vector Machine or Autoencoder) to delineate LGE regions that were initially over-segmented into super-pixel patches. Although this method achieved high accuracy in LA scars segmentation fully automatically, the scar boundaries and continuity of the LA scars in 3D could be affected due to this 2D slice by slice processing.…”

Section: Segmentation Of the La Scarmentioning

confidence: 99%

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

Yang

Chen

Gao

et al. 2020

Future Generation Computer Systems

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Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3DLGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0.27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.

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

Cited by 14 publications

References 34 publications

Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor

Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor

Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

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