International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Advanced Left Ventricle Segmentation from Cardiac MRI Using U-Net with MobileNetV3 Encoder

Rafeef Khalid HasanDepartment of Computer and Communications, Faculty of Engineering, Islamic University of Lebanon, Wardanieh, LebanonAlaa GhaithDepartment of Computer and Communications, Faculty of Engineering, Islamic University of Lebanon, Wardanieh, Lebanon

Vol 8 No 7 (2024): Volume 8, Issue 7, July 2024 | Pages: 82-88

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 22-07-2024

doi Logo doi.org/10.47001/IRJIET/2024.807008

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Abstract

Assessing cardiac function and diagnosing different heart illnesses rely on accurate left ventricle (LV) identification using cardiac magnetic resonance imaging (MRI). To efficiently and accurately segment the left ventricle from 2D cardiac MRI data, this study introduces a novel method that combines a U-Net model with a MobileNetV3 encoder. The ACDC dataset, which includes MRI images and associated ground truth masks, underwent rigorous preprocessing and hyperparameters were adjusted to improve model performance. The evaluation resulted in an average dice score of 92.13%, with the LV segment receiving a dice score of 96.16%, displaying greater performance compared to previous studies. The combination of MobileNetV3 and U-Net has been proven to be effective for medical image segmentation, thereby enhancing diagnostic procedures and ultimately improving patient outcomes.

Keywords

Left Ventricle, Segmentation, Magnetic Resonance Imaging, U-Net, MobileNetV3, ACDC, Dice Score


Citation of this Article

Rafeef Khalid Hasan, & Alaa Ghaith. (2024). Advanced Left Ventricle Segmentation from Cardiac MRI Using U-Net with MobileNetV3 Encoder. International Research Journal of Innovations in Engineering and Technology - IRJIET, 8(7), 82-88, Article DOI https://doi.org/10.47001/IRJIET/2024.807008

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References
  1. O. Gaidai, Y. Cao, and S. Loginov, “Global cardiovascular diseases death rate prediction,” Current Problems in Cardiology, vol. 48, no. 5, 2023.
  2. D. Zhao, G. M. Quill, K. Gilbert, V. Y. Wang, H. C. Houle, M. E. Legget, and M. P. Nash, M. “Systematic comparison of left ventricular geometry between 3D-echocardiography and cardiac magnetic resonance imaging,” Frontiers in Cardiovascular Medicine, vol. 8, 2021.‏
  3. M. M. Hadhoud, M. I. Eladawy, A. Farag, F. M. Montevecchi, and U. Morbiducci, “Left ventricle segmentation in cardiac MRI images,” American Journal of Biomedical Engineering, vol. 2, no. 3, pp. 131-135, 2012.‏
  4. J. Sander, B. D. de Vos, and I.Išgum, “Automatic segmentation with detection of local segmentation failures in cardiac MRI,” Scientific Reports, vol. 10, no. 1, p. 21769, 2020.‏
  5. S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 7, pp. 3523-3542, 2021.‏
  6. M. B. Calisto, and S. K. Lai-Yuen, “AdaEn-Net: An ensemble of adaptive 2D–3D Fully Convolutional Networks for medical image segmentation,” Neural Networks, vol. 126, pp. 76-94, 2020.‏
  7. B. Wu, Y. Fang, and X. Lai, “Left ventricle automatic segmentation in cardiac MRI using a combined CNN and U-net approach,” Computerized Medical Imaging and Graphics, vol. 82, 2020.‏
  8. F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods, vol. 18, no. 2, pp. 203-211, 2020.‏
  9. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.‏
  10. J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, and Y. Zhou, “Transunet: Transformers make strong encoders for medical image segmentation,” arXiv preprint arXiv:2102.04306, 2021. ‏
  11. H. Y. Zhou, J. Guo, Y. Zhang, L. Yu, L. Wang, and Y. Yu, “nnformer: Interleaved transformer for volumetric segmentation,” arXiv preprint arXiv:2109.03201, 2021.‏
  12. H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation,” arXiv e-prints, arXiv-2105, 2021.‏
  13. G. Xu, X. Wu, X. Zhang, and X. He, “LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation,” arXiv preprint arXiv:2107.08623, 2021.‏
  14. P. S. Kavyashree, and M. El-Sharkawy, “Compressed mobilenet v3: a light weight variant for resource-constrained platforms,” In 2021 IEEE 11th annual computing and communication. Workshop and conference (CCWC), pp. 0104-0107, 2021.

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