A novel myocardium segmentation approach based on neutrosophic active contour model

Guo, Yanhui; Du, Guoqing; Xue, Jiangyu; Xia, Rong; Wang, Yuhang

doi:10.1016/j.cmpb.2017.02.020

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…Other approach, proposed by Guo et al [16], presents an automatic myocardial segmentation method based on active contours and measures of neutrosophic similarity, a neutrophysiologic active contour model (NACM). The method uses a tissue clustering approach to detect the left ventricle region.…”

Section: Left-ventricle Segmentation Overviewmentioning

confidence: 99%

An Automatic Left Ventricle Segmentation on Echocardiogram Exams via Morphological Geodesic Active Contour with Adaptive External Energy

Medeiros¹,

Silva²,

Ohata³

et al. 2019

AIS

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This work proposes a new adaptive approach to left ventricle segmentation based on a non-parametric adaptive active contour method called Fast Morphological Geodesic Active Contour (FGAC) combined with adaptive external energy via deep learning model. The evaluation methodology considered echocardiogram exams obtained from volunteers. Beyond the manual segmentations made by two specialists medical as ground truth. The new approach is compared with three other segmentation methods, also based on the active contour method: pSnakes, radial snakes with derivative (RSD), and radial snakes with Hilbert energy (RSH). The FGAC combined with adaptive external energy showed better Precision (99.53%, 99.72%) against RSD (99.46%, 99.68%), RSH (99.51%, 99.71%) and pSnakes (99.52%, 99.72%). Besides, it achieved a relevant Jaccard similarity index (67.40%, 62.02%), and promising accuracy (98.64%, 98.46%). Even though the metrics differences are low, the proposed approach is fully automatic. Therefore, these results suggest the potential of the proposed approach to aid medical diagnosis systems in echocardiology.

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Section: Left-ventricle Segmentation Overviewmentioning

confidence: 99%

An Automatic Left Ventricle Segmentation on Echocardiogram Exams via Morphological Geodesic Active Contour with Adaptive External Energy

Medeiros¹,

Silva²,

Ohata³

et al. 2019

AIS

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“…Currently, there exist two approaches for automatic MYO segmentation. In the traditional MYO segmentation approach (7,8), a manually defined contour or boundary is needed for initialization. Although an automatic initialization might be achieved by some algorithms (9,10), the segmentation performance highly relies on the initialization quality, which makes the framework lack stability.…”

Section: Introductionmentioning

confidence: 99%

Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis

Chen¹,

Xu²,

Zeng³

et al. 2022

Front. Cardiovasc. Med.

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Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences. As manual segmentation is tedious, time-consuming, and with low replicability, automatic MYO segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium. Specifically, we propose to combine conventional neural networks and recurrent neural networks to incorporate temporal information between sequences to ensure temporal consistency. We evaluated our framework on the automated cardiac diagnosis challenge (ACDC) dataset. The experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in the Dice coefficient.

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“…Nonetheless, the application of ML models for the detection of MI using echocardiography is still in its infancy. Many classical ML models rely on describing new myocardium features-this requires manual tracing and multiple trials, thus limiting performance and generalization [25][26][27]. Deep learning, a type of arti cial neural network, can automatically process larger and more complex data sets using technologies such as convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

Deep Polar Residual Network for Identifying Myocardial Infarction in Mce Imaging

Shen

Xue

Guo

et al. 2020

Preprint

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Background: Ultrasonic diagnosis of myocardial infarction (MI) varies markedly among radiologists, and is also time-consuming. In addition, there is a high level of inter-observer variation in diagnosis of MI. The purpose of this study is to develop a deep learning-based model that automatically recognizes MI in myocardial contrast echocardiography(MCE) images, and to compare the model performance against radiologists with varying experience in echocardiography.Methods: In this study, a polar residual network (PResNet) model was employed to recognize MI segments in MCE images of Sprague-Dawley (SD) rats. All SD rats were randomly divided into the MI model group (n=15) and the sham group (n=10). Rats in MI model group were infarcted via left anterior descending coronary artery ligation. Ultimately, MCE was performed on SD rats to observe their myocardium. Left ventricle (LV) short-axis views of MCE at the basal and midpapillary muscle levels for each SD rat were divided into 6 segments separately. The PResNet model and six radiologists each diagnosed 276 MCE image segments. Masson’s trichrome staining was used as the gold standard for MI presence or absence. The PResNet model and radiologist diagnostic performance were evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Furthermore, analysis duration per rat and interobserver variability were also compared. Results: Masson’s trichrome stain revealed 30 infarcted segments out of the 276 total segments. The PResNet model achieved an AUC of 0.89 (0.85-0.93), which was comparable to that of senior radiologists (0.89; 0.85-0.93). Relative to senior radiologists, the PResNet model presented slightly lower sensitivity but significantly higher specificity. The mean sensitivity and specificity for the junior radiologists were significantly lower than both the PResNet model and the senior radiologists. In terms of analysis duration, the PResNet model (148-169sec) was markedly faster than both junior (314-366sec) and senior (193-235sec). Moreover, the intrasample agreement for the PResNet model (k=0.96) was better than the interobserver agreement for the junior (k=0.81) and senior radiologists (k=0.86). Conclusion: The PResNet model not only improved diagnostic performance and observer consensus but also improve the diagnose speed. It is a promising tool for identifying MI.

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A novel myocardium segmentation approach based on neutrosophic active contour model

Abstract: The proposed method can automatically detect myocardium accurately, and is helpful for clinical therapeutics to measure myocardial perfusion and infarct size.

Cited by 14 publications

References 38 publications

An Automatic Left Ventricle Segmentation on Echocardiogram Exams via Morphological Geodesic Active Contour with Adaptive External Energy

An Automatic Left Ventricle Segmentation on Echocardiogram Exams via Morphological Geodesic Active Contour with Adaptive External Energy

Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis

Deep Polar Residual Network for Identifying Myocardial Infarction in Mce Imaging

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