Automated Cardiac MR Scar Quantification in Hypertrophic Cardiomyopathy Using Deep Convolutional Neural Networks

Fahmy, Ahmed S.; Rausch, Johannes; Neisius, Ulf; Chan, Raymond H.; Maron, Martin S.; Appelbaum, Evan; Menze, Bjoern; Nezafat, Reza

doi:10.1016/j.jcmg.2018.04.030

Cited by 60 publications

(40 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LV LGE images were obtained using a three dimensional (3D) phase sensitive inversion-recovery (PSIR) sequence with spectral fat saturation pre-pulses during the end-diastolic phase approximately 15 minutes after administration of 0.1 mmol/kg body weight gadobenate dimeglumine (Multihance, Bracco Diagnostics Inc., Monroe Township, New Jersey, US). LGE presence and percentage (%LGE) were measured by an experienced (level 3 trained) reader (U.N.) blinded to clinical and laboratory data using an automated LV contour and LGE area quantification algorithm [34]. Accurate measurements were assured by visual review of all contours.…”

Section: Methodsmentioning

confidence: 99%

Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy

et al. 2019

Self Cite

View full text Add to dashboard Cite

Background Hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM) are both associated with an increased left ventricular (LV) wall thickness. Whilst LV ejection fraction is frequently normal in both, LV strain assessment could differentiate between the diseases. We sought to establish if cardiovascular magnetic resonance myocardial feature tracking (CMR-FT), an emerging method allowing accurate assessment of myocardial deformation, differentiates between both diseases. Additionally, CMR assessment of fibrosis and LV hypertrophy allowed association analyses and comparison of diagnostic capacities. Methods Two-hundred twenty-four consecutive subjects (53 HHD, 107 HCM, and 64 controls) underwent 1.5T CMR including native myocardial T 1 mapping and late gadolinium enhancement (LGE). Global longitudinal strain (GLS) was assessed by CMR-FT (CVi42, Circle Cardiovascular Imaging Inc.). Results GLS was significantly higher in HCM patients (-14.7±3.8 vs. -16.5±3.3% [HHD], P = 0.004; or vs. -17.2±2.0% [controls], P<0.001). GLS was associated with LV mass index (HHD, R = 0.419, P = 0.002; HCM, R = 0.429, P<0.001), and LV ejection fraction (HHD, R = -0.493, P = 0.002; HCM, R = -0.329, P<0.001). In HCM patients, GLS was also associated with global native T 1 (R = 0.282, P = 0.003), and LGE volume (ρ = 0.380, P<0.001). Discrimination between HHD and HCM by GLS (c = 0.639, 95% confidence interval [CI] 0.550–0.729) was similar to LV mass index (c = 0.643, 95% CI 0.556–0.731), global myocardial native T 1 (c = 0.718, 95% CI 0.638–0.799), and LGE volume (c = 0.680, 95% CI 0.585–0.775). Conclusion CMR-FT GLS differentiates between HHD and HCM. In HCM patients GLS is associated with myocardial fibrosis. The discriminatory capacity of CMR-FT GLS is similar to LV hypertrophy and fibrosis imaging markers.

show abstract

Section: Methodsmentioning

confidence: 99%

Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the work of Fahmy et al (2018), the authors applied a U-net based network to segment the myocardium and the scars at the same time from LGE images acquired from patients with hypertrophic cardiomyopathy (HCM), achieving a fast segmentation speed. However, the reported segmentation accuracy for the scar regions was relatively low (mean Dice: 0.58).…”

Section: Scar Segmentationmentioning

confidence: 99%

Deep Learning for Cardiac Image Segmentation: A Review

Chen

Qiu

et al. 2020

Front. Cardiovasc. Med.

548

337

View full text Add to dashboard Cite

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.

show abstract

“…For the control group, visual inspection was used to exclude the presence of LGE. For the HCM group, LGE was quantified using an automated LV contour and LGE area quantification algorithm specifically developed for LGE quantification in HCM patients [29]. For the DCM group, LGE was quantified using a five standard deviation approach and CVi42 (Circle Cardiovascular Imaging Inc. Calgary, Canada).…”

Section: Plos Onementioning

confidence: 99%

Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

et al. 2020

Self Cite

View full text Add to dashboard Cite

Background The pattern of myocardial fibrosis differs significantly between different cardiomyopathies. Fibrosis in hypertrophic cardiomyopathy (HCM) is characteristically as patchy and regional but in dilated cardiomyopathy (DCM) as diffuse and global. We sought to investigate if texture analyses on myocardial native T 1 mapping can differentiate between fibrosis patterns in patients with HCM and DCM. Methods We prospectively acquired native myocardial T 1 mapping images for 321 subjects (55±15 years, 70% male): 65 control, 116 HCM, and 140 DCM patients. To quantify different fibrosis patterns, four sets of texture descriptors were used to extract 152 texture features from native T 1 maps. Seven features were sequentially selected to identify HCM-and DCM-specific patterns in 70% of data (training dataset). Pattern reproducibility and generalizability were tested on the rest of data (testing dataset) using support vector machines (SVM) and regression models. Results Pattern-derived texture features were capable to identify subjects in HCM, DCM, and controls cohorts with 202/237(85.2%) accuracy of all subjects in the training dataset using 10fold cross-validation on SVM (AUC = 0.93, 0.93, and 0.93 for controls, HCM and DCM, respectively), while pattern-independent global native T 1 mapping was poorly capable to identify those subjects with 121/237(51.1%) accuracy (AUC = 0.78, 0.51, and 0.74) (P<0.001 for all). The pattern-derived features were reproducible with excellent intra-and inter-observer reliability and generalizable on the testing dataset with 75/84(89.3%) accuracy.

show abstract

Automated Cardiac MR Scar Quantification in Hypertrophic Cardiomyopathy Using Deep Convolutional Neural Networks

Cited by 60 publications

References 3 publications

Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy

Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy

Deep Learning for Cardiac Image Segmentation: A Review

Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

Contact Info

Product

Resources

About