Classification of cardiac adipose tissue using spectral analysis of ultrasound radiofrequency backscatter

Karlapalem, Akhila; Givan, Amy; Fernández-del-Valle, María; Fulton, Miranda R.; Klingensmith, Jon D.

doi:10.1117/12.2512972

Cited by 3 publications

(7 citation statements)

References 13 publications

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“…In contrast to the manual definition of Regions of Interest (ROIs) in a prior study 18 , this study employed an automatic definition of ROIs around the complete perimeter of the heart predicted by the Resnet34 model for the PSAX images.…”

Section: Regions-of-interest For Classificationmentioning

confidence: 99%

“…Prior studies have demonstrated feasibility of classifying ROIs in echocardiograms using spectral analysis of raw radiofrequency (RF) data 18 . Building on these methods, this paper proposes a methodology that combines deep-learningpowered echocardiogram segmentation with spectral RF signal analysis to identify and quantify CAT from short-axis images.…”

Section: Introductionmentioning

confidence: 99%

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Echocardiogram image segmentation and cardiac adipose tissue estimation using spectral analysis and deep learning

Cuellar Buritica,

Gillette,

Dinh

et al. 2024

Medical Imaging 2024: Ultrasonic Imaging and Tomography

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Cardiovascular disease (CVD) is the main cause of death worldwide. Cardiac adipose tissue (CAT) around the heart correlates with CVD risk. MRI and CT scans are preferred for CAT quantification. Ultrasound (US) is limited to linear CAT thickness measurements on the right ventricle. Comprehensive CAT volume and distribution quantification with US could aid CVD assessment. This study uses deep learning to automatically segment the left myocardium (LM), left epicardium (LE), and right epicardium (RE) boundaries in echocardiograms. A U-Net ResNet34 model was trained using 506 expert-traced images. Data was split into 70/10/20 training/validation/test sets. The model achieved Dice scores of 0.94, 0.89, and 0.88 for the three boundaries respectively. Regions-of-interest (ROIs) around the combined left and right epicardium contours were classified as containing CAT or not using spectral analysis of raw RF data. MRI expert-traced images of the same patients provided ground truth labels for CAT. A total of 102 corresponding US-MRI image pairs were matched using visual assessment and anatomical landmarks. ROIs were defined around the predicted epicardium contours, and for each ROI, 9 spectral parameters were computed as a random forest classifier input. A randomized 75/25 training/test split was used. The classifier achieved an average accuracy of 76%, sensitivity of 85%, and specificity of 74%. Results show the feasibility of performing automatic CAT assessment involving echocardiogram segmentation, contour detection, and classification of ROIs around the perimeter of the left and right epicardium in short-axis echocardiograms.

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Section: Regions-of-interest For Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Echocardiogram image segmentation and cardiac adipose tissue estimation using spectral analysis and deep learning

Cuellar Buritica,

Gillette,

Dinh

et al. 2024

Medical Imaging 2024: Ultrasonic Imaging and Tomography

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“…However, backscatter has limited ability to reflect fibrosis in those with lower levels of myocardial fibrosis, such as coronary artery disease (Prior et al 2015). In Karlapalem and Givan (2019), the frequency content from echocardiography and spectral analysis techniques were investigated for differentiating three different cardiac tissue types (cardiac adipose tissue, myocardium, and blood). Recently texture features of myocardium have been extracted from still ultrasound images for tissue characterization (Kagiyama et al 2020).…”

Section: Cardiologymentioning

confidence: 99%

“…In Kumon et al (2009), spectral features including intercept, slope, and MBF were considered for the HIFU lesion characterization. To differentiate different cardiac tissue types, thirteen spectral parameters were computed from the power spectrum of the RF data in three different bandwidth ranges (Karlapalem and Givan 2019). Autoregressive models of order 4 were used as they provide effective estimates of the power spectrum for short-time data.…”

Section: Rf-signal-based Approachesmentioning

confidence: 99%

“…Random forests were also exploited for prostate cancer diagnosis (Uniyal et al 2014). In a recent work (Karlapalem and Givan 2019), thirteen spectral parameters derived from the power spectrum of the RF data were used in generating random forests for cardiac tissue classification. The random forest classifier with 50 classification trees resulted in an overall accuracy of 92.4%, sensitivity of 91.1%, specificity of 93.9%.…”

Section: Classifier Designmentioning

confidence: 99%

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Ultrasound tissue classification: a review

et al. 2020

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Ultrasound imaging is the most widespread medical imaging modality for creating images of the human body in clinical practice. Tissue classification in ultrasound has been established as one of the most active research areas, driven by many important clinical applications. In this paper, we present a survey on ultrasound tissue classification, focusing on recent advances in this area. We start with a brief review on the main clinical applications. We then introduce the traditional approaches, where the existing research on feature extraction and classifier design are reviewed. As deep learning approaches becoming popular for medical image analysis, the recent deep learning methods for tissue classification are also introduced. We briefly discuss the FDA-cleared techniques being used clinically. We conclude with the discussion on the challenges and research focus in future.

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An Accurate and Fast Cardio-Views Classification System Based on Fused Deep Features and LSTM

Shahin

Almotairi

2020

IEEE Access

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Echocardiography is an ultrasound-based imaging modality that helps the physician to visualize heart chambers and valves motion activity. Recently, deep learning plays an important role in several clinical computer-assisted diagnostic systems. There is a real need to employ deep learning methodologies to increase such systems. In this paper, we proposed a deep learning system to classify several echocardiography views and identify its physiological location. Firstly, the spatial CNN features are extracted from each frame in the echo-motion. Secondly, we proposed novel temporal features based on neutrosophic sets. The neutrosophic temporal motion features are extracted from echo-motion activity. To extract the deep CNN features, we activated a pre-trained deep ResNet model. Then, both spatial and neutrosophic temporal CNN features were fused based on features concatenation technique. Finally, the fused CNN features were fed into deep long short-term memory network to classify echo-cardio views and identify their location. During our experiments, we employed a public echocardiography dataset that consisted of 432 videos for eight cardio-views. We have investigated several pre-trained network activation performances. ResNet architecture activation achieved the best accuracy score among several pre-trained networks. The Proposed system based on fused spatial neutrosophic temporal deep features achieved 96.3% accuracy and 95.75% sensitivity. For the classification of cardio-views location, the proposed system achieved 99.1% accuracy. The proposed system achieved more accuracy than previous deep learning methods with a significant decrease in the training time cost. The experimental results showed promising results for our proposed approach.

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Classification of cardiac adipose tissue using spectral analysis of ultrasound radiofrequency backscatter

Cited by 3 publications

References 13 publications

Echocardiogram image segmentation and cardiac adipose tissue estimation using spectral analysis and deep learning

Echocardiogram image segmentation and cardiac adipose tissue estimation using spectral analysis and deep learning

Ultrasound tissue classification: a review

An Accurate and Fast Cardio-Views Classification System Based on Fused Deep Features and LSTM

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