Version 1
: Received: 17 May 2024 / Approved: 17 May 2024 / Online: 21 May 2024 (10:35:43 CEST)
How to cite:
Balasubramani, M.; Sung, C.-W.; Hsieh, M.-Y.; Huang, E. P.-C.; Shieh, J.-S.; Abbod, M. Automated Left Ventricle Segmentation in Echocardiography using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment. Preprints2024, 2024051196. https://doi.org/10.20944/preprints202405.1196.v1
Balasubramani, M.; Sung, C.-W.; Hsieh, M.-Y.; Huang, E. P.-C.; Shieh, J.-S.; Abbod, M. Automated Left Ventricle Segmentation in Echocardiography using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment. Preprints 2024, 2024051196. https://doi.org/10.20944/preprints202405.1196.v1
Balasubramani, M.; Sung, C.-W.; Hsieh, M.-Y.; Huang, E. P.-C.; Shieh, J.-S.; Abbod, M. Automated Left Ventricle Segmentation in Echocardiography using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment. Preprints2024, 2024051196. https://doi.org/10.20944/preprints202405.1196.v1
APA Style
Balasubramani, M., Sung, C. W., Hsieh, M. Y., Huang, E. P. C., Shieh, J. S., & Abbod, M. (2024). Automated Left Ventricle Segmentation in Echocardiography using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment. Preprints. https://doi.org/10.20944/preprints202405.1196.v1
Chicago/Turabian Style
Balasubramani, M., Jiann-Shing Shieh and Maysam Abbod. 2024 "Automated Left Ventricle Segmentation in Echocardiography using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment" Preprints. https://doi.org/10.20944/preprints202405.1196.v1
Abstract
Accurate segmentation of the Left Ventricle (LV) in Echocardiogram (Echo) images is essential for cardiovascular analysis. Conventional techniques are labor-intensive and exhibit inter-observer variability. Deep learning has emerged as a powerful tool for automated medical image segmentation, offering advantages in speed and potentially superior accuracy. This study explores the efficacy of employing the YOLO (You Only Look Once) segmentation model for automated LV segmentation in Echo images.
YOLO, a cutting-edge object detection model, achieves exceptional speed-accuracy balance through its well-designed architecture. It utilizes efficient dilated convolutional layers and bottleneck blocks for feature extraction, while incorporating innovations like path aggregation and spatial attention mechanisms. These attributes make YOLO a compelling candidate for adaptation to LV segmentation in Echo images. We posit that by fine-tuning a pre-trained YOLO based model on a well-annotated Echo image dataset, we can leverage the model's strengths in real-time processing and precise object localization to achieve robust LV segmentation.
The proposed approach entails fine-tuning a pre-trained YOLO model on a rigorously labeled Echo image dataset. Model performance has been evaluated using established metrics such as mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 50% (mAP50) with 98.31%and across a range of IoU thresholds from 50% to 95% (mAP50:95) with 75.27%.
Successful implementation of YOLO for LV segmentation has the potential to significantly expedite and standardize Echo image analysis. This advancement could translate to improved clinical decision-making and enhanced patient care.
Keywords
Left Ventricle(LV); echocardiogram; deep learning; segmentation; feature extraction; dilated convolution; YOLO
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
