TY - GEN
T1 - Modified MultiResUNet for Left Ventricle Segmentation from Echocardiographic Images
AU - Azizi, Fityan
AU - Sani, Akbar Fathur
AU - Priambodo, Rinto
AU - Karunianto, Wisma Chaerul
AU - Ramadhan, Mgs M.Luthfi
AU - Rachmadi, Muhammad Febrian
AU - Jatmiko, Wisnu
N1 - Funding Information:
We gratefully acknowledge the support from Tokopedia-UI AI Center, Faculty of Computer Science, University of Indonesia, for the used of NVIDIA DGX-1 for running the experiments.
Funding Information:
We gratefully acknowledge the support from TokopediaUI AI Center, Faculty of Computer Science, University of Indonesia, for the used of NVIDIA DGX-1 for running the experiments.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - An accurate assessment of heart function is crucial in diagnosing the cardiovascular disease. One way to evaluate or detect the disease can use echocardiography, by detecting systolic and diastolic volumes. However, manual human assessments can be time-consuming and error-prone due to the low resolution of the image. One way to detect heart failure on echocardiogram is by segmenting the left ventricle on the echocardiogram using deep learning. In this study, we modified the MultiResUNet model for left ventricle segmentation in echocardiography images by adding Atrous Spatial Pyramid Pooling block and Attention block. The use of multires blocks from MultiResUnet is able to overcome the problem of multi-resolution segmentation objects, where the segmentation objects have different sizes. This problem has similar characteristics to echocardiographic images, where the systole and diastole segmentation objects have different sizes from each other. Performance measure were evaluated using Echonet-Dynamic dataset. The proposed model achieves dice coefficient of 92%, giving an additional 2% performance result compared to the MultiResUNet.
AB - An accurate assessment of heart function is crucial in diagnosing the cardiovascular disease. One way to evaluate or detect the disease can use echocardiography, by detecting systolic and diastolic volumes. However, manual human assessments can be time-consuming and error-prone due to the low resolution of the image. One way to detect heart failure on echocardiogram is by segmenting the left ventricle on the echocardiogram using deep learning. In this study, we modified the MultiResUNet model for left ventricle segmentation in echocardiography images by adding Atrous Spatial Pyramid Pooling block and Attention block. The use of multires blocks from MultiResUnet is able to overcome the problem of multi-resolution segmentation objects, where the segmentation objects have different sizes. This problem has similar characteristics to echocardiographic images, where the systole and diastole segmentation objects have different sizes from each other. Performance measure were evaluated using Echonet-Dynamic dataset. The proposed model achieves dice coefficient of 92%, giving an additional 2% performance result compared to the MultiResUNet.
KW - Deep Learning
KW - Echocardiography
KW - Heart Function
KW - Semantic Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85141850617&partnerID=8YFLogxK
U2 - 10.1109/IWBIS56557.2022.9924685
DO - 10.1109/IWBIS56557.2022.9924685
M3 - Conference contribution
AN - SCOPUS:85141850617
T3 - IWBIS 2022 - 7th International Workshop on Big Data and Information Security, Proceedings
SP - 33
EP - 38
BT - IWBIS 2022 - 7th International Workshop on Big Data and Information Security, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Workshop on Big Data and Information Security, IWBIS 2022
Y2 - 1 October 2022 through 3 October 2022
ER -
