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Identification of the left ventricle endocardial border on two-dimensional ultrasound images using deep layer aggregation for residual dense networks

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Abstract

Ultrasound images are one of the most widely used medical images in clinical medicine. However, ultrasound images generally have the characteristics of strong noise, weak edges, and complex organizational structures, so the segmentation of ultrasound images is very difficult. Aiming at the problems of low efficiency and poor recognition accuracy in the existing ultrasound image segmentation algorithm, a deep layer aggregation for a residual dense network (DLA-RDNet) is proposed to segment the left ventricle ultrasound images. First, to locate the left ventricular region, perform morphological operations on the left ventricular ultrasound image to obtain the target image. Then, a deep aggregation residual for dense networks is proposed for left ventricular ultrasound image segmentation, that is, a residual dense network (RDNet) is designed to extract image features, and the feature information is closely integrated through the deep aggregation (DLA) method. Finally, the network is pruned through the deep supervision (DS) method, which simplifies the network structure and improves the network operation speed. We segment the left ventricular ultrasound images by the proposed algorithm. The experimental results show that the average precision rate(AP) is 95.68%, the average intersection over union (IoU) is 97.13%, the Dice coefficient is 97.15%, the average perpendicular distance(APD) is 0.31 mm, and the good contours rate (GC) is 99.32%. Compared with the other six segmentation algorithms, the proposed algorithm achieves a more effective segmentation of the left ventricle ultrasound images. Therefore, the proposed algorithm can accurately segment the left ventricle ultrasound images, which meets the needs of the segmentation of the left ventricle ultrasound images in clinical medicine.

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Acknowledgments

This work was supported by the Research Project on Science and Technology of Chongqing Education Committee in 2020 (KJQN202005803, KJQN202005801), the Seventh Batch of Pilot Projects of Comprehensive Education Reform in Chongqing in 2021 (21JGS63), the Demonstration Course of Ideological and Political of Chongqing Vocational College of Public Transportation in 2021 (KCSZ-21-01), Research Project of Vocational College of Public Transportation in 2020 (YSKY2020-04). The authors thank the Editor-in-Chief, the Associate Editor, and the anonymous reviewers for their constructive comments and suggestions.

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Authors and Affiliations

  1. Chongqing Vocational College of Public Transportation, Chongqing, 402247, China

    Xuanyan Wu, Gang Mou, Dechun Wang, Yan He & Zhengdong Li

  2. Communication NCO Academy, Army Engineering University of PLA, Chongqing, 400035, China

    Xiuling Li

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  1. Xuanyan Wu
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Correspondence to Xiuling Li.

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Wu, X., Li, X., Mou, G. et al. Identification of the left ventricle endocardial border on two-dimensional ultrasound images using deep layer aggregation for residual dense networks. Appl Intell 52, 16089–16103 (2022). https://doi.org/10.1007/s10489-022-03392-9

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