Left vertical segmentation of 2-D heart MRI images using U-net network
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Published:
Nov 14, 2023
Keywords:
Image processing, MRI, Segmentation, U-Net
Section
Computer Science
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Abstract
Medical image processing has benefited greatly from advances in artificial intelligence, especially through the use of artificial neural networks. U-Net is an artificial neural network architecture that has proven successful in medical image segmentation tasks. Therefore, this study aims to combine the power of U-Net networks with 2-D cardiac MRI images to achieve accurate and automatic left vertical segmentation of the heart. By having a tool that can automatically segment the left vertical heart, doctors and researchers will be able to save valuable time in medical image analysis, while increasing accuracy and consistency in the assessment of heart structure. The results of this research will contribute to technological developments in the field of cardiovascular medicine and improve the care of patients with heart disease. Based on the work that was done and the results that were reported in Tabel I, the accuracy at the time of validation for full heart coroners, core heart coroners, and augmenting heart coroners was, respectively, 90.22%.
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How to Cite
Rachmat, R., Kasmawaru, K., Rafli R, M. ., Yusuf, M. . and Basmar, M. F. . (2023) “Left vertical segmentation of 2-D heart MRI images using U-net network”, Jurnal Mantik, 7(3), pp. 2082-2086. doi: 10.35335/mantik.v7i3.4306.
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Xu, S., Lu, H., Cheng, S., & Pei, C. (2022). Left Ventricle Segmentation in Cardiac MR Images via an Improved ResUnet. 2022.
Yasmin, F., Shah, S. M. I., Naeem, A., Shujauddin, S. M., Jabeen, A., Kazmi, S., Siddiqui, S. A., Kumar, P., Salman, S., & Hassan, S. A. (2021). Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future. Reviews in Cardiovascular Medicine, 22(4), 1095–1113.
Yin, S., & Wang, Y. (2022). Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning. 1–12.
Zhou, L.-Q., Wang, J.-Y., Yu, S.-Y., Wu, G.-G., Wei, Q., Deng, Y.-B., Wu, X.-L., Cui, X.-W., & Dietrich, C. F. (2019). Artificial intelligence in medical imaging of the liver. World Journal of Gastroenterology, 25(6), 672.
Anwar, S. M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., & Khan, M. K. (2018). Medical image analysis using convolutional neural networks: a review. Journal of Medical Systems, 42, 1–13.
Bagus, I., Mahadya, L., Sudarma, M., Kumara, I. N. S., & Optimizer, A. (2020). Resonance Imaging Dengan Menggunakan Metode U-NET. 19(2), 151–156.
Basyid, F., Adi, K., Sains, F., & Diponegoro, U. (2014). SEGMENTASI CITRA MEDIS UNTUK PENGENALAN OBJEK KANKER MENGGUNAKAN METODE ACTIVE CONTOUR. 3(3), 209–216.
Charmchi, S. (2018). Optimized U-Net for Left Ventricle Segmentation by Sadegh Charmchi A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Computing Science University of Alberta.
Elektronika, P., & Surabaya, N. (2020). Medical image processing. 1–35.
Experts, D., Advisors, P., Bobar, M., & Reina, T. (n.d.). Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning.
Gomathi, G. (2022). DPA-UNet?: Detail preserving attention UNet for cardiac MRI ventricle region segmentation. 6(April), 11833–11852.
Goshal, D., & Acharjya, P. P. (2012). MRI image segmentation using watershed transform. Int. J. of Emerging Technology and Advanced Engineering, 2(4), 373–376.
Hariyadi, M. A. (n.d.). SEGMENTASI CITRA X-RAY THORAX MENGGUNAKAN LEVEL SET.
Irshad, M., Yasmin, M., Sharif, M. I., Rashid, M., Sharif, M. I., & Kadry, S. (2023). A Novel Light U-Net Model for Left Ventricle Segmentation Using MRI.
Jantung, F. P. (2013). Interpretasi Hasil Pemeriksaan MRI Kardiak pada Penyakit Jantung Koroner. 34(1).
Jolly, M.-P. (2006). Automatic segmentation of the left ventricle in cardiac MR and CT images. International Journal of Computer Vision, 70(2), 151–163.
Liao, F., Chen, X., Hu, X., Member, S., & Song, S. (2015). Estimating the volume of the left ventricle from MRI images using deep neural networks. 14(8), 1–10.
Ma, X., Hadjiiski, L. M., Wei, J., Chan, H., Cha, K. H., Cohan, R. H., Caoili, E. M., Samala, R., Zhou, C., & Lu, Y. (2019). U?Net based deep learning bladder segmentation in CT urography. Medical Physics, 46(4), 1752–1765.
Mehdy, M. M., Ng, P. Y., Shair, E. F., Saleh, N. I., & Gomes, C. (2017). Artificial neural networks in image processing for early detection of breast cancer. Computational and Mathematical Methods in Medicine, 2017.
MENGGUNAKAN METODE HOMOTOPY TREE Oleh?: Kunti Eliyen. (2013).
Narayanan, A., Rajasekaran, M. P., Zhang, Y., Govindaraj, V., & Thiyagarajan, A. (2019). Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation. Biocybernetics and Biomedical Engineering, 39(2), 350–381.
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021). Carbon emissions and large neural network training. ArXiv Preprint ArXiv:2104.10350.
Ramadhan, E., Komputer, F. I., & Sriwijaya, U. (2010). Identifikasi Kelainan Jantung Menggunakan Pola Citra Digital Electrocardiogram. 5(1).
Seo, B., Mariano, D., Beckfield, J., Madenur, V., & Hu, Y. (2019). Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning. October.
Slama, S., Mahmoudi, R., Hmida, B., Maatouk, M., Hedi, M., Slama, S., Mahmoudi, R., Hmida, B., Maatouk, M., Hedi, M., & Right, B. (2022). Right Ventricle Segmentation in Cardiac MR Images Using Convolutional Neural Network Architecture To cite this version?: HAL Id?: hal-03844844 Right Ventricle Segmentation in Cardiac MR Images Us- ing Convolutional Neural Network Architecture.
Tsadok, Y., Petrank, Y., Sarvari, S., Edvardsen, T., & Adam, D. (2013). Automatic segmentation of cardiac MRI cines validated for long axis views. Computerized Medical Imaging and Graphics, 37(7–8), 500–511.
Valente, A. M., Cook, S., Festa, P., Ko, H. H., Krishnamurthy, R., Taylor, A. M., Warnes, C. A., Kreutzer, J., & Geva, T. (2014). Multimodality imaging guidelines for patients with repaired tetralogy of Fallot: a report from the American Society of Echocardiography: developed in collaboration with the Society for Cardiovascular Magnetic Resonance and the Society for Pediatric Radiology. Journal of the American Society of Echocardiography, 27(2), 111–141.
Wang, Y., Li, S., Huang, J., & Lai, Q. (n.d.). Cardiac MRI segmentation of the atria based on UU-NET.
Widyantara, I. M. O., Tommy, A., Prawira, A., Made, N., Esta, A., Wirastuti, D., & Pendahuluan, I. (2015). Preprocessing pada Segmentasi Citra Paru-Paru dan Jantung Menggunakan Anisotropic Diffusion Filter. 14(2), 6–10.
Xu, S., Lu, H., Cheng, S., & Pei, C. (2022). Left Ventricle Segmentation in Cardiac MR Images via an Improved ResUnet. 2022.
Yasmin, F., Shah, S. M. I., Naeem, A., Shujauddin, S. M., Jabeen, A., Kazmi, S., Siddiqui, S. A., Kumar, P., Salman, S., & Hassan, S. A. (2021). Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future. Reviews in Cardiovascular Medicine, 22(4), 1095–1113.
Yin, S., & Wang, Y. (2022). Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning. 1–12.
Zhou, L.-Q., Wang, J.-Y., Yu, S.-Y., Wu, G.-G., Wei, Q., Deng, Y.-B., Wu, X.-L., Cui, X.-W., & Dietrich, C. F. (2019). Artificial intelligence in medical imaging of the liver. World Journal of Gastroenterology, 25(6), 672.
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