Abstract
Left ventricular segmentation for transthoracic echocardiographic (TTE) is crucial for advanced diagnosis of cardiovascular disease and measurement of cardiac function parameters. Recently, some TTE ventricular segmentation methods achieved satisfactory performance with large amounts of labeled data. However, the process of labeling medical segmentation data requires specialist surgeons and is highly time-consuming. To reduce reliance on segmentation annotations, we propose a label-free approach for left ventricular segmentation for TTE named LF-LVS. Specifically, we design multiple sets of templates and employ three common data enhancement strategies to generate pseudo-ultrasound masks and their corresponding pseudo-ground truths (pseudo-GTs). Then, we utilize CycleGAN with real-world TTE images to construct a synthetic transthoracic echocardiographic left ventricular segmentation dataset (STE-LVS), which will play an important role in the research of TTE left ventricular segmentation. Finally, we feed both the synthetic and real-world TTE data into a weight-shared segmentation network, and devise a domain adaptation discriminator to ensure their similarity in the output space of the segmentation network. Extensive experiments demonstrate the effectiveness of our proposed LF-LVS, which achieves satisfactory performance on the EchoNet-Dynamic dataset without any annotation. Our STE-LVS dataset and code are available at https://github.com/SCUT-BIP-Lab/LF-LVS.
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Acknowledgement
This work was supported by the Fundamental Research Funds for the Central Universities under Grant 2022ZYGXZR099, the National Natural Science Foundation of China under Grant 61976095 and the Natural Science Foundation of Guangdong Province, China, under Grant 2022A1515010114.
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Kang, Q., Tang, W., Liu, Z., Kang, W. (2024). LF-LVS: Label-Free Left Ventricular Segmentation for Transthoracic Echocardiogram. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14437. Springer, Singapore. https://doi.org/10.1007/978-981-99-8558-6_37
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DOI: https://doi.org/10.1007/978-981-99-8558-6_37
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