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Article Dans Une Revue Australasian Physical and Engineering Sciences in Medicine Année : 2022

Left ventricle analysis in echocardiographic images using transfer learning

Résumé

The segmentation of cardiac boundaries, specifically Left Ventricle (LV) segmentation in 2D echocardiographic images, is a critical step in LV segmentation and cardiac function assessment. These images are generally of poor quality and present low contrast, making daily clinical delineation difficult, time-consuming, and often inaccurate. Thus, it is necessary to design an intelligent automatic endocardium segmentation system. The present work aims to examine and assess the performance of some deep learning-based architectures such as U-Net1, U-Net2, LinkNet, Attention U-Net, and TransUNet using the public CAMUS (Cardiac Acquisitions for Multi-structure Ultrasound Segmentation) dataset. The adopted approach emphasizes the advantage of using transfer learning and resorting to pre-trained backbones in the encoder part of a segmentation network for echocardiographic image analysis. The experimental findings indicated that the proposed framework with the U-Net1VGG19 is quite promising; it outperforms other more recent approaches with a Dice similarity coefficient of 93.30% and a Hausdorff Distance of 4.01 mm. In addition, a good agreement between the clinical indices calculated from the automatic segmentation and those calculated from the ground truth segmentation. For instance, the mean absolute errors for the left ventricular end-diastolic volume, end-systolic volume, and ejection fraction are equal to 7.9 ml, 5.4 ml, and 6.6%, respectively. These results are encouraging and point out additional perspectives for further improvement.
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Dates et versions

hal-03790749 , version 1 (28-09-2022)

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Hafida Belfilali, Frédéric Bousefsaf, Mahammed Messadi. Left ventricle analysis in echocardiographic images using transfer learning. Australasian Physical and Engineering Sciences in Medicine, 2022, 45, pp.1123-1138. ⟨10.1007/s13246-022-01179-3⟩. ⟨hal-03790749⟩
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