Segmentation of the Left Ventricle from Ultrasound Using Random Forest with Active Shape Model

Belous, Gregg; Busch, Andrew; Rowlands, D. D.

doi:10.1109/aims.2013.58

Cited by 14 publications

(10 citation statements)

References 13 publications

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“…AI also helps the physicians automatically to classify several cardio-views [49][50]. AI detected several cardio-pathological diseases such as wall motion disorders [51], detection of left ventricle disorders [52], mitral regurgitation [53]. AI also helps the physicians to quantify several cardiac-motion parameters such as: MV (Myocardial velocity) [54], EF (ejection fraction) [55], and LS (longitudinal strain) [56].…”

Section: Literature Reviewmentioning

confidence: 99%

“…They utilized high-morphological descriptors, then they employed support vector machine and it achieved 87% sensitivity, and 82% specificity for 139 patients included with their patient history. In [59], the authors presented an automated system to detect the left ventricle based on the active contour algorithm and random forest classifier. The system achieved 90% accuracy for only 85 images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The previous studies showed that traditional machine learning methods consume a lot of time to extract handcrafted features and are very sensitive to motion artifacts [53][54][55][56][57][58][59][60]. On the other hand, deep learning methods based on training CNN from scratch consume a lot of time, which reached to a few days, and there is still a challenge to increase its accuracy [49].…”

Section: Literature Reviewmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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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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“…Because of high correlated noise and possible poor image quality, this task may be quite difficult. Artificial algorithms based on deep learning architectures can be used for automatic segmentation of the ventricle walls [94,100–102] or the detection of the bounding box containing heart valves in 2D echocardiography [103]. Recently published work shows promising results of utilizing speckle-tracking data and ML for automated discrimination of hypertrophic cardiomyopathy from physiological hypertrophy seen in athletes [104].…”

Section: Machine Learningmentioning

confidence: 99%

Cardiac imaging: working towards fully-automated machine analysis & interpretation

Slomka

Dey

Sitek

et al. 2017

Expert Review of Medical Devices

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Introduction Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

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“…In the case of echocardiography, physicians are required to undergo a long training process which has a steep learning curve (44,45). Furthermore, during interpretation, image quality can be affected by speckle noise, low contrast and signal dropouts which interfere with diagnosis (9,46). Apart from that, the complex spatio-temporal motion of the heart can cause difficulties in interpretation (5).…”

Section: Rural Healthcare Telemedicine and Mobile Health (Mhealth)mentioning

confidence: 99%

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

Loh¹,

Then²

2017

mHealth

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Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.

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Segmentation of the Left Ventricle from Ultrasound Using Random Forest with Active Shape Model

Cited by 14 publications

References 13 publications

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

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

Cardiac imaging: working towards fully-automated machine analysis & interpretation

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

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