Automatic segmentation of echocardiographic left ventricular images by windows adaptive thresholds

Santos, Jaime; Celorico, Dirce; Varandas, J.; Dias, J.M.B.

doi:10.3728/icultrasonics.2007.vienna.1680_santos

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…The threshold-based segmentation methods are simple and effective, dividing the image into various regions with obvious contrast [3]. Santos et al [4] used the Otsu threshold method to adaptively adjust the threshold value of left ventricular long-axis ultrasound images to extract the endocardial contour. However, the complex images cannot be effectively processed via the threshold-based segmentation methods, which are sensitive to mild changes in noise.…”

Section: Traditional Segmentation Methodsmentioning

confidence: 99%

Automatic Segmentation and Assessment of Valvular Regurgitations with Color Doppler Echocardiography Images: A VABC-UNet-Based Framework

Huang,

et al. 2023

Bioengineering

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This study investigated the automatic segmentation and classification of mitral regurgitation (MR) and tricuspid regurgitation (TR) using a deep learning-based method, aiming to improve the efficiency and accuracy of diagnosis of valvular regurgitations. A VABC-UNet model was proposed consisting of VGG16 encoder, U-Net decoder, batch normalization, attention block and deepened convolution layer based on the U-Net backbone. Then, a VABC-UNet-based assessment framework was established for automatic segmentation, classification, and evaluation of valvular regurgitations. A total of 315 color Doppler echocardiography images of MR and/or TR in an apical four-chamber view were collected, including 35 images in the test dataset and 280 images in the training dataset. In comparison with the classic U-Net and VGG16-UNet models, the segmentation performance of the VABC-UNet model was evaluated via four metrics: Dice, Jaccard, Precision, and Recall. According to the features of regurgitation jet and atrium, the regurgitation could automatically be classified into MR or TR, and evaluated to mild, moderate, moderate–severe, or severe grade by the framework. The results show that the VABC-UNet model has a superior performance in the segmentation of valvular regurgitation jets and atria to the other two models and consequently a higher accuracy of classification and evaluation. There were fewer pseudo- and over-segmentations by the VABC-UNet model and the values of the metrics significantly improved (p < 0.05). The proposed VABC-UNet-based framework achieves automatic segmentation, classification, and evaluation of MR and TR, having potential to assist radiologists in clinical decision making of the regurgitations in valvular heart diseases.

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Section: Traditional Segmentation Methodsmentioning

confidence: 99%

Automatic Segmentation and Assessment of Valvular Regurgitations with Color Doppler Echocardiography Images: A VABC-UNet-Based Framework

Huang,

et al. 2023

Bioengineering

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“…Automating this procedure of heart chamber segmentation saves time by providing rapid, precise, and objective segmentation throughout the cardiac cycle. To perform segmentation, certain conventional methods for image processing have been proposed, including Otsu thresholding and edge detection [ 15 ], a watershed algorithm for LV border segmentation [ 16 ] and K-means clustering [ 17 ]. These techniques are computationally efficient, but they have a high signal-to-noise ratio and fail to produce acceptable results when there are unclear borders and non-uniform regional intensities.…”

Section: Related Workmentioning

confidence: 99%

Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods

2023

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Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson’s method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.

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“…In another study for the segmentation of LV in echo images was done by Santos et al (2007). Their preprocessing method reduced the data size and processing time through cropping image regions into fixed sizes and then dividing the image into rectangular blocks.…”

Section: Jcsmentioning

confidence: 99%

“…Therefore, pre-processing is an important stage in feature extraction for echocardiography images. The pre-processing stage can influence the accuracy of ROI segmentation as well as the time cost of boundary extraction Santos et al (2007).…”

Section: Rv Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Cavity Segmentation and Stroke Volume Measurement Methods From Echocardiographic Images: Review

Abboud¹,

Rahmat²,

Kadiman³

et al. 2014

Journal of Computer Science

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The complexity and criticality of heart disease diagnosis and treatment have frequently attracted the attention of researchers to develop approaches that can handle accurate clinical assessments. One of these complexities is measuring the Right Ventricle stroke volume. Therefore, different cardiac imaging techniques have been developed to help specialists in their regular clinical work and in the operating theatre to assist in the diagnosis, monitoring of the treatment and to ensure the success of a cardiac intervention as far as possible. Echocardiography is one of the cardiac imaging techniques and is considered as the most effective imaging technique as it is minimally invasive, quick and the cheapest method. Many studies have been conducted on Right Ventricle assessment from echocariographic images. This review is aimed at presenting the previous technical elements and algorithms used to measure the ventricle stroke volume from echocardiographic images. The review focuses on the five technical approaches that have been used, which are pre-processing, End-Distally (ED) and End-Systolic (ES) frame detection, cavity segmentation, the stoke volume measurement and 3D reconstruction. Finally, a summary will conclude this study.

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Automatic segmentation of echocardiographic left ventricular images by windows adaptive thresholds

Cited by 9 publications

References 8 publications

Automatic Segmentation and Assessment of Valvular Regurgitations with Color Doppler Echocardiography Images: A VABC-UNet-Based Framework

Automatic Segmentation and Assessment of Valvular Regurgitations with Color Doppler Echocardiography Images: A VABC-UNet-Based Framework

Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods

Cavity Segmentation and Stroke Volume Measurement Methods From Echocardiographic Images: Review

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