Fully automated multi-heartbeat echocardiography video segmentation and motion tracking

Chen, Yida; Zhang, Xiaoyan; Haggerty, Christopher M; Stough, Joshua V.

doi:10.1117/12.2607871

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Ouyang et al [8] 0.9270 Deng et al [9] 0.9164 Saeed et al [12] 0.9252 El rai et al [13] 0.9389 Puyol-Antón et al [14] 0.9350 Chen et al [15] 0.9440…”

Section: References Dice Similarity Coefficientmentioning

confidence: 99%

“…For example, based on segmenting the entire cardiac cycle, Puyol-Antón et al [14] developed an AI-based model for obtaining advanced systolic and diastolic LV function biomarkers. In addition, Chen et al [15] Accurate US segmentation of cardiac anatomy currently relies on sonographers, resulting in tedious jobs reliant on manual competency [4]. In addition, it is challenging for human operation due to low contrast, speckle noise, and poorly defined boundaries [5].…”

Section: Introductionmentioning

confidence: 99%

“…For example, based on segmenting the entire cardiac cycle, Puyol-Antón et al [14] developed an AI-based model for obtaining advanced systolic and diastolic LV function biomarkers. In addition, Chen et al [15] employed a modified R2 + 1 D ResNet stem to construct a fully automated framework that offered motion tracking apart from joint semantic segmentation in multi-beat echocardiograms. This convolutional network operates sliding windows to accommodate the heart cycle while efficiently encoding spatiotemporal data.…”

Section: Introductionmentioning

confidence: 99%

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Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

Kumar

Song

et al. 2023

Life

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Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads to observer bias. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, on the downside, it still ignores the contribution of all semantic information through the segmentation process. This study proposes a deep neural network architecture based on BiSeNet, named Bi-DCNet. This model comprises a spatial path and a context path, with the former responsible for spatial feature (low-level) acquisition and the latter responsible for contextual semantic feature (high-level) exploitation. Moreover, it incorporates feature extraction through the integration of dilated convolutions to achieve a larger receptive field to capture multi-scale information. The EchoNet-Dynamic dataset was utilized to assess the proposed model, and this is the first bilateral-structured network implemented on this large clinical video dataset for accomplishing the segmentation of the left ventricle. As demonstrated by the experimental outcomes, our method obtained 0.9228 and 0.8576 in DSC and IoU, respectively, proving the structure’s effectiveness.

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“…Ouyang et al [8] 0.9270 Deng et al [9] 0.9164 Saeed et al [12] 0.9252 El rai et al [13] 0.9389 Puyol-Antón et al [14] 0.9350 Chen et al [15] 0.9440…”

Section: References Dice Similarity Coefficientmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For example, based on segmenting the entire cardiac cycle, Puyol-Antón et al [14] developed an AI-based model for obtaining advanced systolic and diastolic LV function biomarkers. In addition, Chen et al [15] employed a modified R2 + 1 D ResNet stem to construct a fully automated framework that offered motion tracking apart from joint semantic segmentation in multi-beat echocardiograms. This convolutional network operates sliding windows to accommodate the heart cycle while efficiently encoding spatiotemporal data.…”

Section: Introductionmentioning

confidence: 99%

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Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

Kumar

Song

et al. 2023

Life

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“…By using straightforward image processing methods like segmentation and tracking, these indices can be extracted [8]. However, accurate delineation of cardiac structures, particularly the left ventricular endocardium (LV endo ), during end diastole (ED) and end systole (ES), is essential for the precise extraction of parameters such as the ejection fraction (EF ) of the left ventricle (LV ) [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of 2-D Echocardiography Ultrasound Images by Means of Generative Adversarial Network

Fatima,

Afrakhteh,

Iacca

et al. 2024

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Automated cardiac segmentation from twodimensional (2D) echocardiographic images is a crucial step toward improving clinical diagnosis. Anatomical heterogeneity and inherent noise, however, present technical challenges and lower segmentation accuracy. The objective of this study is to propose a method for the automatic segmentation of the ventricular endocardium, the myocardium, and the left atrium, in order to accurately determine clinical indices. Specifically, we suggest using the recently introduced pixel-to-pixel Generative Adversarial Network (Pix2Pix GAN) model for accurate segmentation. To accomplish this, we integrate the backbone PatchGAN model for the discriminator and the UNET for the generator, for building the Pix2Pix GAN. The resulting model produces precisely segmented images, thanks to UNET's capability for precise segmentation and PatchGAN's capability for fine-grained discrimination. For the experimental validation, we use the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset, which consists of echocardiographic images from 500 patients in 2-chamber (2CH) and 4-chamber (4CH) views at the end-diastolic (ED) and end-systolic (ES) phases. Similarly to state-of-the-art studies on the same dataset, we followed the same train-test splits. Our results demonstrate that the proposed GAN-based technique improves segmentation performance for clinical and geometrical parameters compared to the state-of-the-art methods. More precisely, throughout the ED and ES phases, the mean Dice values for the left ventricular endocardium reached 0.961 and 0.930 for 2CH, and 0.959 and 0.950 for 4CH, respectively. Furthermore, the average ejection fraction correlation and Mean Absolute Error obtained were 0.95 and 3.2ml for 2CH, and 0.98 and 2.1ml for 4CH, outperforming the state-of-the-art results.

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Feature Tensor Low-Rank Representation Network for Semi-Supervised Echocardiography Video Left Ventricle Segmentation

Li,

Hu,

2024

2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Fully automated multi-heartbeat echocardiography video segmentation and motion tracking

Cited by 5 publications

References 17 publications

Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

Automatic Segmentation of 2-D Echocardiography Ultrasound Images by Means of Generative Adversarial Network

Feature Tensor Low-Rank Representation Network for Semi-Supervised Echocardiography Video Left Ventricle Segmentation

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