Left Ventricle Segmentation in Echocardiography with Transformer

Article Acoustics

MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography

Yan Zeng et al.

Summary: In this study, a deep learning-based fully automated echocardiographic analysis method was proposed to automatically segment the left ventricle and detect cardiac phases for calculating left ventricular ejection fraction (LVEF). The validation results on public and private datasets demonstrated high accuracy in left ventricular segmentation and cardiac phase detection, with small errors in LVEF prediction.

ULTRASONICS (2023)

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Proceedings Paper Computer Science, Artificial Intelligence

TransBridge: A Lightweight Transformer for Left Ventricle Segmentation in Echocardiography

Kaizhong Deng et al.

Summary: A lightweight model named 'TransBridge' was developed for left ventricle segmentation on echocardiography images, combining CNN and transformer structures. The model achieved impressive results by reducing parameter number and optimizing the embedding layer.

SIMPLIFYING MEDICAL ULTRASOUND (2021)

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Article Computer Science, Information Systems

ResDUnet: A Deep Learning-Based Left Ventricle Segmentation Method for Echocardiography

Alyaa Amer et al.

Summary: The study proposed a fully automated left ventricle segmentation method named ResDUnet, which achieved outstanding performance in overcoming the fuzziness of echocardiographic images through integrated cascaded dilated convolution for feature extraction.

IEEE ACCESS (2021)

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Article Multidisciplinary Sciences