Ultrasound in medicine & biology · 2026
Echo-EU-Net: Lightweight Deep Learning for Fully Automated Left Ventricular Segmentation and Ejection Fraction Quantification in Transgastric Short-Axis View on Transesophageal Echocardiography.
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Ling Yijun, Tian Yuan, Qin Wenting, Leonov Denis, Ding Huapeng, Wang Chunrong, Tian Yajie, Zhang Yuelun, He Kai, Zhang Yuguan, Shen Le, Yu Chunhua, Zhou Zhuhuang
Аннотация
OBJECTIVE: Deep learning-based automated analysis of transgastric short-axis view (TSV) transesophageal echocardiography (TEE) remains under-explored. In this study, we propose a deep learning-based method for fully automated left ventricular segmentation and ejection fraction (EF) prediction in TSV TEE videos.
METHODS: We built upon the U-Net network and proposed an Echo Efficient U-Net (Echo-EU-Net) segmentation model by replacing the original standard convolutions with depth-wise separable convolutions and by introducing the Multi-Efficient Channel Attention (MECA) and Enhanced Atrous Spatial Pyramid Pooling (EASPP) modules. We also incorporated automatic cardiac phase tracking and EF calculation. Experiments were performed on a TSV TEE dataset containing 694 videos from 451 patients, with expert manual segmentations and manual EF measurements as the reference standard.
RESULTS: The proposed Echo-EU-Net, with an average Dice similarity coefficient of 92.91% and a Jaccard similarity coefficient of 87.23%, outperformed U-Net and its variants for left ventricular segmentation in TSV TEE, particularly in challenging cases. The model parameter size of Echo-EU-Net was 1.30 million, compared with 7.79 million for U-Net. The proposed EF prediction method had a satisfying agreement with the manual EF measurements (Pearson's r=0.84), with a mean absolute error of 6.44%. An ablation study demonstrated the effectiveness of the MECA and EASPP modules.
CONCLUSION: The feasibility of the proposed Echo-EU-Net-based method in automatically segmenting the left ventricle and measuring EF in TSV TEE has been demonstrated. The findings of this study may shed light on lightweight deep learning-based fully automated left ventricular segmentation and EF quantification in TSV TEE.
METHODS: We built upon the U-Net network and proposed an Echo Efficient U-Net (Echo-EU-Net) segmentation model by replacing the original standard convolutions with depth-wise separable convolutions and by introducing the Multi-Efficient Channel Attention (MECA) and Enhanced Atrous Spatial Pyramid Pooling (EASPP) modules. We also incorporated automatic cardiac phase tracking and EF calculation. Experiments were performed on a TSV TEE dataset containing 694 videos from 451 patients, with expert manual segmentations and manual EF measurements as the reference standard.
RESULTS: The proposed Echo-EU-Net, with an average Dice similarity coefficient of 92.91% and a Jaccard similarity coefficient of 87.23%, outperformed U-Net and its variants for left ventricular segmentation in TSV TEE, particularly in challenging cases. The model parameter size of Echo-EU-Net was 1.30 million, compared with 7.79 million for U-Net. The proposed EF prediction method had a satisfying agreement with the manual EF measurements (Pearson's r=0.84), with a mean absolute error of 6.44%. An ablation study demonstrated the effectiveness of the MECA and EASPP modules.
CONCLUSION: The feasibility of the proposed Echo-EU-Net-based method in automatically segmenting the left ventricle and measuring EF in TSV TEE has been demonstrated. The findings of this study may shed light on lightweight deep learning-based fully automated left ventricular segmentation and EF quantification in TSV TEE.