Segmentation of left ventricle in 2D echocardiography using deep learning

Neda, Azarmehr, Xujiong, Ye and Zolgharni, Massoud ORCID: (2020) Segmentation of left ventricle in 2D echocardiography using deep learning. In: 23rd Conference on Medical Image Understanding and Analysis, 24-26 July 2019, Liverpool.

[thumbnail of Zolgharni_Xujiong_and_Neda_MIUA_2019_Segmentation_of_left_ventricle_in_2D_echocardiography_using_deep_learning.pdf]
Zolgharni_Xujiong_and_Neda_MIUA_2019_Segmentation_of_left_ventricle_in_2D_echocardiography_using_deep_learning.pdf - Accepted Version

Download (328kB) | Preview


The segmentation of Left Ventricle (LV) is currently carried out manually by the experts, and the automation of this process has proved challenging due to the presence of speckle noise and the inherently poor quality of the ultrasound images. This study aims to evaluate the performance of different state-of-the-art Convolutional Neural Network (CNN) segmentation models to segment the LV endocardium in echocardiography images automatically. Those adopted methods include U-Net, SegNet, and fully convolutional DenseNets (FC-DenseNet). The prediction outputs of the models are used to assess the performance of the CNN models by comparing the automated results against the expert annotations (as the gold standard). Results reveal that the U-Net model outperforms other models by achieving an average Dice coefficient of 0.93 ± 0.04, and Hausdorff distance of 4.52 ± 0.90

Item Type: Conference or Workshop Item (Poster)
ISSN: 1865-0929
ISBN: 9783030393427
Identifier: 10.1007/978-3-030-39343-4_43
Page Range: pp. 497-504
Identifier: 10.1007/978-3-030-39343-4_43
Subjects: Computing > Intelligent systems
Related URLs:
Depositing User: Massoud Zolgharni
Date Deposited: 23 Sep 2019 07:07
Last Modified: 28 Aug 2021 07:11


Downloads per month over past year

Actions (login required)

View Item View Item