Liver Segmentation Using Convolutional Neural Network Method with U-Net Architecture
Authors
Muhammad Awaludin Djohar , Anita Desiani , Ali Amran , Sugandi Yahdin , Dewi Lestari Dwi Putri , Des Alwine Zayanti , Novi Rustiana DewiDOI:
10.31289/jite.v6i1.6751Published:
2022-07-23Issue:
Vol. 6 No. 1 (2022): Issues July 2022Keywords:
Segmentation, U-Net, Liver, CNNDownloads
Abstract
Abnormalities in the liver can be used to identify the occurrence of disorders of the liver, one of which is called liver cancer. To detect abnormalities in the liver, segmentation is needed to take part of the liver that is affected. Segmentation of the liver is usually done manually with x-rays. . This manual detection is quite time consuming to get the results of the analysis. Segmentation is a technique in the image processing process that allocates images into objects and backgrounds. Deep learning applications can be used to help segment medical images. One of the deep learning methods that is widely used for segmentation is U-Net CNN. U-Net CNN has two parts encoder and decoder which are used for image segmentation. This research applies U-Net CNN to segment the liver data image. The performance results of the application of U-Net CNN on the liver image are very goodAccuracy performance obtained is 99%, sensitivity is 99%. The specificity is 99%, the F1-Score is 98%, the Jacard coefficient is 96.46% and the DSC is 98%. Â The performance achieved from the application of U-Net CNN on average is above 95%, it can be concluded that the application of U-Net CNN is very good and robust in segmenting abnormalities in the liver. This study only discusses the segmentation of the liver image. The results obtained have not been applied to the classification of types of disorders that exist in the liver yet. Further research can apply the segmentation results from the application of U-Net CNN in the problem of classifying types of liver disorders.References
Anter, A. M., & Hassenian, A. E. (2019). CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm. Artificial Intelligence in Medicine, 97(September), 105–117. https://doi.org/10.1016/j.artmed.2018.11.007
Bilic, P., Christ, P. F., Vorontsov, E., Chlebus, G., Chen, H., Dou, Q., Fu, C.-W., Han, X., Heng, P.-A., Hesser, J., Kadoury, S., Konopczynski, T., Le, M., Li, C., Li, X., Lipkovà , J., Lowengrub, J., Meine, H., Moltz, J. H., … Menze, B. H. (2019). The Liver Tumor Segmentation Benchmark (LiTS). 1–43. http://arxiv.org/abs/1901.04056
Bilic, P., Christ, P. F., Vorontsov, E., Chlebus, G., Lowengrub, J., Meine, H., Moltz, J. H., Pal, C., & Piraud, M. (2019). The Liver Tumor Segmentation Benchmark ( LiTS ). MICCAI Workshop, 1–43.
Cai, J. (2019). Segmentation and Diagnosis of Liver Carcinoma Based on Adaptive Scale-Kernel Fuzzy Clustering Model for CT Images. Journal of Medical Systems, 43(11). https://doi.org/10.1007/s10916-019-1459-2
Desiani, A., Yahdin, S., & Kartikasari, A. (2021). Handling the imbalanced data with missing value elimination SMOTE in the classification of the relevance education background with graduates employment. 10(2), 346–354. https://doi.org/10.11591/ijai.v10.i2.pp346-354
Desiani, A., Zayanti, D. A., Primartha, R., Efriliyanti, F., & Andriani, N. A. C. (2021). Variasi Thresholding untuk Segmentasi Pembuluh Darah Citra Retina. Jurnal Edukasi Dan Penelitian Informatika, 7(2), 255–262.
Es-Sabery, F., Hair, A., Qadir, J., Sainz-De-Abajo, B., Garcia-Zapirain, B., & Torre-DIez, I. (2021). Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier. IEEE Access, 9(February), 17943–17985. https://doi.org/10.1109/ACCESS.2021.3053917
Fikri, A. D. (2019). Perbandingan Metode Dice Similarity dengan Cosine Similarity Menggunakan Query Expansion pada Pencarian Ayatul Ahkam dalam Terjemah Al-Qur’an Berbahasa Indonesia. Universitas Islam Negeri Maulana Malik Ibrahim.
Guo, T., Dong, J., & Li, H. (2017). Simple Convolutional Neural Network on Image Classification. IEEE International Conference on Big Data Analysis, 721–724.
Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y. W., & Wu, J. (2020). UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2020-May(ii), 1055–1059.
https://doi.org/10.1109/ICASSP40776.2020.9053405
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C. W., & Heng, P. A. (2018). H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes. IEEE Transactions on Medical Imaging, 37(12), 2663–2674. https://doi.org/10.1109/TMI.2018.2845918
Lian, S., Li, L., Lian, G., Xiao, X., Luo, Z., & Li, S. (2019). A Global and Local Enhanced Residual U-Net for Accurate Retinal Vessel Segmentation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14(8), 1–10. https://doi.org/10.1109/tcbb.2019.2917188
Litbang, B. (2019). Liver Disebut Penyebab Kematian Terbesar di Usia 35-49 Tahun. Badan Litbang.
Popat, V., Mahdinejad, M., Dalmau Cedeño, O. S., Naredo, E., & Ryan, C. (2020). GA-based U-Net architecture optimization applied to retina blood vessel segmentation. IJCCI 2020 - Proceedings of the 12th International Joint Conference on Computational Intelligence, Ijcci, 192–199. https://doi.org/10.5220/0010112201920199
Shamsaldin, A., Fattah, P., Rashid, T., & Al-Salihi, N. (2019). A Study of The Convolutional Neural Networks Applications. UKH Journal of Science and Engineering, 3(2), 31–40. https://doi.org/10.25079/ukhjse.v3n2y2019.pp31-40
Siddique, N., Paheding, S., Elkin, C. P., & Devabhaktuni, V. (2021). U-Net and its variants for medical image segmentation: A review of theory and applications. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3086020
Soomro, T. A., Afifi, A. J., Zheng, L., Soomro, S., Gao, J., Hellwich, O., & Paul, M. (2019). Deep Learning Models for Retinal Blood Vessels Segmentation: A Review. IEEE Access, 7, 71696–71717. https://doi.org/10.1109/ACCESS.2019.2920616
Triwoto, A. R., & Sardjono, T. A. (2015). Rekonstruksi 3d Citra Magnetic Resonancy Imaging ( Mri ) Abdomen Untuk Identifikasi Polip Pada Saluran Pencernaan. 404–410.
Valanarasu, J. M. J., Sindagi, V. A., Hacihaliloglu, I., & Patel, V. M. (2020). KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation. X(X), 1–14. http://arxiv.org/abs/2010.01663
Wang, M., & Deng, W. (2018). Deep visual domain adaptation: A survey. Neurocomputing, 312(April), 135–153. https://doi.org/10.1016/j.neucom.2018.05.083
Xie, G. Sen, Zhang, X. Y., Yan, S., & Liu, C. L. (2017). Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation. IEEE Transactions on Circuits and Systems for Video Technology, 27(6), 1263–1274. https://doi.org/10.1109/TCSVT.2015.2511543
Zhou, W., Ma, X., & Zhang, Y. (2020). Research on Image Preprocessing Algorithm and Deep Learning of Iris Recognition. Journal of Physics: Conference Series, 1621(1). https://doi.org/10.1088/1742-6596/1621/1/012008
License
This work is licensed under aCreative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).