Mid-level image representations for real-time heart view plane classification of echocardiograms

Penatti, Otávio Augusto Bizetto; Werneck, Rafael de Oliveira; Almeida, Waldir Rodrigues de; Stein, Bernardo V.; Pazinato, Daniel V.; Júnior, Pedro Ribeiro Mendes; Torres, Ricardo da Silva; Rocha, Anderson

doi:10.1016/j.compbiomed.2015.08.004

Cited by 18 publications

(8 citation statements)

References 41 publications

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“…This method has an average classification accuracy of 77.1% for the eight main sections. Penatti et al [ 18 ] used SVM and backpropagation neural networks to classify echocardiograms based on the gray histogram and statistical features such as entropy, kurtosis, skewness, mean value, and standard deviation, and the average classification accuracy rate was 90%. Khamis et al [ 19 ] proposed the use of discriminative learning dictionaries and spatiotemporal feature extraction and supervised dictionary learning methods to classify the three apical sections (apical two-chamber, apical four-chamber, and apical three-chamber) of echocardiography, and the average classification accuracy is 95%.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning

Zhu

Fang

2021

Journal of Healthcare Engineering

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Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce the risk of this disease, a large number of patients still retain a high risk of experiencing a life-threatening cardiovascular event. Thus, the advent of new treatments methods capable of reducing this residual risk remains an important healthcare objective. This paper proposes a deep learning-based method for section recognition of cardiac ultrasound images of critically ill cardiac patients. A convolution neural network (CNN) is used to classify the standard ultrasound video data. The ultrasound video data is parsed into a static image, and InceptionV3 and ResNet50 networks are used to classify eight ultrasound static sections, and the ResNet50 with better classification accuracy is selected as the standard network for classification. The correlation between the ultrasound video data frames is used to construct the ResNet50 + LSTM model. Next, the time-series features of the two-dimensional image sequence are extracted and the classification of the ultrasound section video data is realized. Experimental results show that the proposed cardiac ultrasound image recognition model has good performance and can meet the requirements of clinical section classification accuracy.

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Section: Related Workmentioning

confidence: 99%

Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning

Zhu

Fang

2021

Journal of Healthcare Engineering

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“…Related work and state of the art Penatti et al (2015) reviewed cardiac view classification for TTE up to 2013. Most studies consider a selection of three or four of the most common cardiac views: apical two chamber (A2C), apical fourchamber (A4C) and apical long-axis (ALAX), as well as the parasternal long-axis (PLAX) and parasternal short-axis (PSAX).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks

Østvik

Smistad

Aase³

et al. 2019

Ultrasound in Medicine & Biology

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Transthoracic echocardiography examinations are usually performed according to a protocol comprising different probe postures providing standard views of the heart. These are used as a basis when assessing cardiac function, and it is essential that the morphophysiological representations are correct. Clinical analysis is often initialized with the current view, and automatic classification can thus be useful in improving today's workflow. In this article, convolutional neural networks (CNNs) are used to create classification models predicting up to seven different cardiac views. Data sets of 2-D ultrasound acquired from studies totaling more than 500 patients and 7000 videos were included. State-of-the-art accuracies of (98.3±0.6)% and (98.9±0.6)% on single frames and sequences, respectively, and real-time performance with (4.4±0.3) ms per frame was achieved. Further, it was found that CNNs have the potential for use in automatic multiplanar reformatting and orientation guidance. Using 3-D data to train models applicable for 2-D classification, we achieved a median deviation of (4±3) • from the optimal orientations.

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“…Hence, there has been a wide interest in DL-based approaches for classifying the view of the heart. Penatti et al [ 46 ] proposed a bag of visual words (BOVW) representation for the classification of four cardiac view planes. A BOVW for an image represents an image as a set of features which consists of keypoints and descriptors.…”

Section: Advanced Us Imaging In Cardiology and DL Techniquesmentioning

confidence: 99%

Advanced Ultrasound and Photoacoustic Imaging in Cardiology

Awasthi

Rad

et al. 2021

Sensors

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Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. An effective management and treatment of CVDs highly relies on accurate diagnosis of the disease. As the most common imaging technique for clinical diagnosis of the CVDs, US imaging has been intensively explored. Especially with the introduction of deep learning (DL) techniques, US imaging has advanced tremendously in recent years. Photoacoustic imaging (PAI) is one of the most promising new imaging methods in addition to the existing clinical imaging methods. It can characterize different tissue compositions based on optical absorption contrast and thus can assess the functionality of the tissue. This paper reviews some major technological developments in both US (combined with deep learning techniques) and PA imaging in the application of diagnosis of CVDs.

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Mid-level image representations for real-time heart view plane classification of echocardiograms

Cited by 18 publications

References 41 publications

Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning

Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning

Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks

Advanced Ultrasound and Photoacoustic Imaging in Cardiology

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