Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium‐enhanced MRI: Towards objective atrial scar assessment

Tao, Qian; İpek, Esra Gücük; Shahzad, Rahil; Berendsen, Floris F.; Nazarian, Saman; Geest, Rob J. van der

doi:10.1002/jmri.25148

Cited by 44 publications

(49 citation statements)

References 22 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Semi-automatic and automatic methods have been proposed to solve this task, e.g., using thresholding with region growing [20], statistical shape model [21] and atlas propagation [22] based approaches. However, these methods required further operator's manual intervention [20,21] or used un-gated first-pass MR angiography (MRA) data [22], which may cause difficulties in co-registration with the respiratory and cardiac gated LGE MRI data.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, maximum intensity projection (MIP) can provide intuitive visualization of the atrial fibrosis [11,12,20,22]; however, this is only a visualization technique for hyper-enhancement regions, rather than a segmentation method that can result in volumetric quantification [21]. Recently, a grand challenge was carried out to benchmark different algorithms for solving AFS [19] including 8 submissions for the competition.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Yang

Zhuang

Khan

et al. 2017

Communications in Computer and Information Science

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yang

Zhuang

Khan

et al. 2017

Communications in Computer and Information Science

View full text Add to dashboard Cite

“…This can partly be attributed to the fact that atrial scarring identification generally relies on manually segmented anatomy of the LA and pulmonary veins (PVs), which is not only timeconsuming but also highly subjective among human operators and different research institutions 3 . The anatomy may be segmented from a respiratory and ECG gated CMRI roadmap acquisition or from rapidly acquired breath-hold MR angiography (MRA) 4,5 . While the latter is faster, the lack of ECG gating leads to mis-registration with the respiratory and ECG gated LGE CMRI dataset and the former is preferred.…”

Section: Introductionmentioning

confidence: 99%

Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI

et al. 2017

View full text Add to dashboard Cite

Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is a non-invasive technique, which has shown promise in detecting native and post-ablation atrial scarring. To visualize the scarring, a precise segmentation of the left atrium (LA) and pulmonary veins (PVs) anatomy is performed as a first step-usually from an ECG gated CMRI roadmap acquisition-and the enhanced scar regions from the LGE CMRI images are superimposed. The anatomy of the LA and PVs in particular is highly variable and manual segmentation is labor intensive and highly subjective. In this paper, we developed a multi-atlas propagation based whole heart segmentation (WHS) to delineate the LA and PVs from ECG gated CMRI roadmap scans. While this captures the anatomy of the atrium well, the PVs anatomy is less easily visualized. The process is therefore augmented by semi-automated manual strokes for PVs identification in the registeredLGE CMRI data. This allows us to extract more accurate anatomy than the fully automated WHS. Both qualitative visualization and quantitative assessment with respect to manual segmented ground truth showed that our method is efficient and effective with an overall mean Dice score of 0.91.

show abstract

“…More recently, Karim et al [12] utilized a statistical shape model to solve the LA and PVs geometry but subject to manual corrections. In [13], an automatic atlas based method has been applied; however, local level set based refinement is required using co-registered MR angiography (MRA) data. MRA data are generally acquired in an inspiratory breath-hold and without cardiac gating; therefore, the anatomy extracted from MRA can be highly deformed compared to that acquired by LGE MRI and this may cause difficulties in the co-registration step and subsequently mistake the SAS.…”

Section: Introductionmentioning

confidence: 99%

“…Several strategies have been proposed for visualization of the atrial scarring, e.g., maximum intensity projection (MIP) [6], [11], [13], [14]. MIP based methods can provide more intuitive visualization and may have a potential role for guiding CA procedures.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Zhuang

Khan

et al. 2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

View full text Add to dashboard Cite

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. This also requires accurate delineation of the left atrium (LA) and pulmonary veins (PVs) geometry. Most previous studies have relied on manual segmentation of LA wall and PVs, which is a tedious and error-prone procedure with limited reproducibility. There are many attempts on automatic SAS using simple thresholding, histogram analysis, clustering and graph-cut based approaches; however, in general, these methods are considered as unsupervised learning thus subject to limited segmentation accuracy. In this study, we present a fully-automated multi-atlas based whole heart segmentation method to derive the LA and PVs geometry objectively that is followed by a fully automatic deep learning method for SAS. Our deep learning method consists of a feature extraction step via super-pixel over-segmentation and a supervised classification step via stacked sparse auto-encoders. We demonstrate the efficacy of our method on 20 clinical LGE MRI scans acquired from a longstanding persistent atrial fibrillation cohort. Both quantitative and qualitative results show that our fully automatic method obtained accurate segmentation results compared to the manual segmentation based ground truths. INTRODUCTIONAtrial fibrillation (AF) is the most common sustained heart rhythm disturbance encountered in adult cardiology. Several studies have shown that AF is correlated with electrical, contractile, and structural remodeling in the left atrium (LA) [1]. Moreover, LA fibrosis may be arrhythmogenic that causes more aggressive symptoms and makes difficulties in the management of AF [1]. Minimally invasive catheter ablation (CA) using radio-frequency energy has become one of the most common treatments for AF patients refractory to drug treatment [2]. CA aims to electrically isolate the pulmonary veins (PVs) from the left atrial (LA) body because previous studies show that ectopic beats from the PVs can frequently trigger the AF [3]. In this context, techniques have been developed to evaluate the LA wall composition and assess the circumferential PVs scarring that results from CA in order to understand the AF with proper management and prognosis. At present, electro-anatomical mapping (EAM) system, which is performed during the electrophysiological study, is considered to be a clinical reference standard technique for the assessment of the LA substrate and ablation-induced scarring. However, EAM is invasive and suffers from ionizing radiation and its suboptimal accuracy, which has reported errors of up to 10 mm in the localization of scar tissue [4]. Noninvasive late gadolinium-enhanced (LGE) MRI is an established method for visualizing and assessing myocardial infarction or fibrosis [5]. This is ascribed to the altered wash-in and wash-out contrast agent kinetics, and the hyper-enhancement reflects the increased interstitial space of the myocardium wit...

show abstract

Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium‐enhanced MRI: Towards objective atrial scar assessment

Cited by 44 publications

References 22 publications

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

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

Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI

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

Contact Info

Product

Resources

About