Pemisahan Objek Sel Tumpang Tindih pada Citra Pap Smear dengan Metode Deep Learning dan Watershed
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Published:
Nov 30, 2022
Keywords:
Object Separation, Cervical Cancer, Pap Smear Image, Overlapping Cells, Segmentation
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Abstract
The object observed in the Pap Smear image is Cervical Cancer which forms overlapping cells. This cancer must be observed early because it is a disease that causes the death of thousands of women worldwide every year. The death rate from this disease is the fourth highest among women. One way to be aware of this disease is to do an early check on the Pap Smear test image. This cell separation process uses the image segmentation method. This method is one of the important steps to be able to identify existing cell objects. This study proposes a segmentation method to separate 2 overlapping cells in the RepomedUNM dataset. The dataset is engineered in the manufacture of synthetic Pap Smear images. The segmentation method proposed is a Deep Learning-based method so that it can identify 2 overlapping cells in one area. The level of accuracy of the test with an average score of Intersection over Union (IoU) is 0.9003. And the results of segmentation with Deep Learning can be divided into all areas using the Watershed segmentation method. So that this research becomes a reference in the early identification of Cervical Cancer.
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Jamil, M., & Riana, D. (2022). Pemisahan Objek Sel Tumpang Tindih pada Citra Pap Smear dengan Metode Deep Learning dan Watershed. Jurnal Informasi Dan Teknologi, 4(4), 253-259. https://doi.org/10.37034/jidt.v4i4.243
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References
[1] Rio, S., & Suci, E. S. T. (2017). Persepsi tentang kanker serviks dan upaya prevensinya pada perempuan yang memiliki keluarga dengan riwayat kanker. Jurnal Kesehatan Reproduksi, 4(3), 159-169. doi: 10.22146/jkr.36511
[2] Wantini, N. A., & Indrayani, N. (2019). Deteksi dini kanker serviks dengan inspeksi visual asam asetat (IVA). Jurnal Ners dan Kebidanan (Journal of Ners and Midwifery), 6(1), 027-034. doi: 10.26699/jnk.v6i1.ART.p027
[3] Riana, D., & Hidayanto, A. N. (2017, August). Integration of Bagging and greedy forward selection on image Pap Smear classification using Naïve Bayes. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-7). IEEE. doi: 10.1109/CITSM.2017.8089320
[4] Hidayatulloh, T., Herliana, A., & Arifin, T. (2016). Klasifikasi sel tunggal Pap Smear berdasarkan analisis fitur berbasis naive bayes classifier dan particle swarm optimization. Swabumi, 4(2), 186-193.
[5] Pasrun, Y. P., Fatichah, C., & Suciati, N. (2016). Penggabungan Fitur Bentuk dan Fitur Tekstur yang Invariant terhadap Rotasi untuk Klasifikasi Citra Pap Smear. Jurnal Buana Informatika, 7(1). doi: 10.24002/jbi.v7i1.479
[6] Husain, N. P., & Fatichah, C. (2017). Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Radiating Component Normalized Generalized GVFS. Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), 6(1), 107-114.
[7] Zhao, M., Wang, H., Han, Y., Wang, X., Dai, H. N., Sun, X., ... & Pedersen, M. (2021). Seens: Nuclei segmentation in Pap Smear images with selective edge enhancement. Future Generation Computer Systems, 114, 185-194. doi: 10.1016/j.future.2020.07.045
[8] Braz, E. F., & Lotufo, R. D. A. (2017). Nuclei detection using deep learning. In Proc. Simpósio Brasileiro Telecomunicações Processamento Sinais (pp. 1059-1063). doi: 10.14209/sbrt.2017.48
[9] Gautam, S., Bhavsar, A., Sao, A. K., & Harinarayan, K. K. (2018, March). CNN based segmentation of nuclei in PAP-smear images with selective pre-processing. In Medical Imaging 2018: Digital Pathology (Vol. 10581, pp. 246-254). SPIE. doi: 10.1117/12.2293526
[10] Harangi, B., Toth, J., Bogacsovics, G., Kupas, D., Kovacs, L., & Hajdu, A. (2019, September). Cell detection on digitized Pap Smear images using ensemble of conventional image processing and deep learning techniques. In 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) (pp. 38-42). IEEE. doi: 10.1109/ISPA.2019.8868683
[11] Araújo, F. H., Silva, R. R., Ushizima, D. M., Rezende, M. T., Carneiro, C. M., Bianchi, A. G. C., & Medeiros, F. N. (2019). Deep learning for cell image segmentation and ranking. Computerized Medical Imaging and Graphics, 72, 13-21. doi: 10.1016/j.compmedimag.2019.01.003
[12] Mahyari, T. L., & Dansereau, R. M. (2021, January). Deep Learning Methods for Image Decomposition of Cervical Cells. In 2020 28th European Signal Processing Conference (EUSIPCO) (pp. 1110-1114). IEEE. doi: 10.23919/Eusipco47968.2020.9287435
[13] Kupas, D., & Harangi, B. (2021, November). Solving the problem of imbalanced dataset with synthetic image generation for cell classification using deep learning. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 2981-2984). IEEE. doi: 10.1109/EMBC46164.2021.9631065
[14] Ibtehaz, N., & Rahman, M. S. (2020). MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural networks, 121, 74-87. doi: 10.1016/j.neunet.2019.08.025
[15] Iglovikov, V., & Shvets, A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746. [Online]. Available: http://arxiv.org/abs/1801.05746
[16] Cheng, D., & Lam, E. Y. (2021). Transfer learning U-Net deep learning for lung ultrasound segmentation. arXiv preprint arXiv:2110.02196. [Online]. Available: http://arxiv.org/abs/2110.02196
[17] Pravitasari, A. A., Iriawan, N., Almuhayar, M., Azmi, T., Irhamah, I., Fithriasari, K., ... & Ferriastuti, W. (2020). UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(3), 1310-1318. doi: 10.12928/TELKOMNIKA.v18i3.14753
[18] Cui, B., Chen, X., & Lu, Y. (2020). Semantic segmentation of remote sensing images using transfer learning and deep convolutional neural network with dense connection. IEEE Access, 8, 116744-116755. doi: 10.1109/ACCESS.2020.3003914
[19] Branch, M. V., & Carvalho, A. S. (2021). Polyp Segmentation in Colonoscopy Images using U-Net-MobileNetV2. arXiv preprint arXiv:2103.15715.
[20] 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. doi: 10.1109/TMI.2018.2845918
[21] Wei, Z., Song, H., Chen, L., Li, Q., & Han, G. (2019). Attention-based DenseUnet network with adversarial training for skin lesion segmentation. IEEE Access, 7, 136616-136629. oi: 10.1109/ACCESS.2019.2940794
[22] Wang, W., Taft, D. A., Chen, Y. J., Zhang, J., Wallace, C. T., Xu, M., ... & Xing, J. (2019). Learn to segment single cells with deep distance estimator and deep cell detector. Computers in biology and medicine, 108, 133-141. doi: 10.1016/j.compbiomed.2019.04.006
[23] Riana, D., Rahayu, S., Hadianti, S., Frieyadie, F., Hasan, M., Karimah, I. N., & Pratama, R. (2022). Identifikasi Citra Pap Smear RepoMedUNM dengan Menggunakan K-Means Clustering dan GLCM. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(1), 1-8. doi: 10.29207/resti.v6i1.3495
[24] Riana, D., Hadianti, S., Rahayu, S., Hasan, M., Karimah, I. N., & Pratama, R. (2021, December). RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images. In International Conference on Neural Information Processing (pp. 317-325). Springer, Cham. doi: 10.1007/978-3-030-92307-5_37
[25] Zhang, J., Hu, Z., Han, G., & He, X. (2016). Segmentation of overlapping cells in cervical smears based on spatial relationship and overlapping translucency light transmission model. Pattern recognition, 60, 286-295. doi: 10.1016/j.patcog.2016.04.021
[26] Lu, Z., Carneiro, G., Bradley, A. P., Ushizima, D., Nosrati, M. S., Bianchi, A. G., ... & Hamarneh, G. (2016). Evaluation of three algorithms for the segmentation of overlapping cervical cells. IEEE journal of biomedical and health informatics, 21(2), 441-450. doi: 10.1109/JBHI.2016.2519686
[27] 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, 9, 82031-82057. doi: 10.1109/ACCESS.2021.3086020
[28] Du, G., Cao, X., Liang, J., Chen, X., & Zhan, Y. (2020). Medical image segmentation based on u-net: A review. Journal of Imaging Science and Technology, 64, 1-12. oi: 10.2352/J.ImagingSci.Technol.2020.64.2.020508
[29] Adiba, A., Hajji, H., & Maatouk, M. (2019, March). Transfer learning and U-Net for buildings segmentation. In Proceedings of the New Challenges in Data Sciences: Acts of the Second Conference of the Moroccan Classification Society (pp. 1-6). doi: 10.1145/3314074.3314088
[30] Karthick, R. (2018). Deep Learning For Age Group Classification System. International Journal Of Advances In Signal And Image Sciences, 4(2), 16-22. doi: 10.29284/ijasis.4.2.2018.16-22
[31] McAuliffe, M. J., Lalonde, F. M., McGarry, D., Gandler, W., Csaky, K., & Trus, B. L. (2001, July). Medical image processing, analysis and visualization in clinical research. In Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001 (pp. 381-386). IEEE. doi: 10.1109/CBMS.2001.941749
[32] Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 658-666). doi: 10.1109/CVPR.2019.00075
[33] Arora, R., Saini, I., & Sood, N. (2021). Multi-label segmentation and detection of COVID-19 abnormalities from chest radiographs using deep learning. Optik, 246, 167780. doi: 10.1016/j.ijleo.2021.167780
[34] Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1-48. doi: 10.1186/s40537-019-0197-0
[2] Wantini, N. A., & Indrayani, N. (2019). Deteksi dini kanker serviks dengan inspeksi visual asam asetat (IVA). Jurnal Ners dan Kebidanan (Journal of Ners and Midwifery), 6(1), 027-034. doi: 10.26699/jnk.v6i1.ART.p027
[3] Riana, D., & Hidayanto, A. N. (2017, August). Integration of Bagging and greedy forward selection on image Pap Smear classification using Naïve Bayes. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-7). IEEE. doi: 10.1109/CITSM.2017.8089320
[4] Hidayatulloh, T., Herliana, A., & Arifin, T. (2016). Klasifikasi sel tunggal Pap Smear berdasarkan analisis fitur berbasis naive bayes classifier dan particle swarm optimization. Swabumi, 4(2), 186-193.
[5] Pasrun, Y. P., Fatichah, C., & Suciati, N. (2016). Penggabungan Fitur Bentuk dan Fitur Tekstur yang Invariant terhadap Rotasi untuk Klasifikasi Citra Pap Smear. Jurnal Buana Informatika, 7(1). doi: 10.24002/jbi.v7i1.479
[6] Husain, N. P., & Fatichah, C. (2017). Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Radiating Component Normalized Generalized GVFS. Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), 6(1), 107-114.
[7] Zhao, M., Wang, H., Han, Y., Wang, X., Dai, H. N., Sun, X., ... & Pedersen, M. (2021). Seens: Nuclei segmentation in Pap Smear images with selective edge enhancement. Future Generation Computer Systems, 114, 185-194. doi: 10.1016/j.future.2020.07.045
[8] Braz, E. F., & Lotufo, R. D. A. (2017). Nuclei detection using deep learning. In Proc. Simpósio Brasileiro Telecomunicações Processamento Sinais (pp. 1059-1063). doi: 10.14209/sbrt.2017.48
[9] Gautam, S., Bhavsar, A., Sao, A. K., & Harinarayan, K. K. (2018, March). CNN based segmentation of nuclei in PAP-smear images with selective pre-processing. In Medical Imaging 2018: Digital Pathology (Vol. 10581, pp. 246-254). SPIE. doi: 10.1117/12.2293526
[10] Harangi, B., Toth, J., Bogacsovics, G., Kupas, D., Kovacs, L., & Hajdu, A. (2019, September). Cell detection on digitized Pap Smear images using ensemble of conventional image processing and deep learning techniques. In 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) (pp. 38-42). IEEE. doi: 10.1109/ISPA.2019.8868683
[11] Araújo, F. H., Silva, R. R., Ushizima, D. M., Rezende, M. T., Carneiro, C. M., Bianchi, A. G. C., & Medeiros, F. N. (2019). Deep learning for cell image segmentation and ranking. Computerized Medical Imaging and Graphics, 72, 13-21. doi: 10.1016/j.compmedimag.2019.01.003
[12] Mahyari, T. L., & Dansereau, R. M. (2021, January). Deep Learning Methods for Image Decomposition of Cervical Cells. In 2020 28th European Signal Processing Conference (EUSIPCO) (pp. 1110-1114). IEEE. doi: 10.23919/Eusipco47968.2020.9287435
[13] Kupas, D., & Harangi, B. (2021, November). Solving the problem of imbalanced dataset with synthetic image generation for cell classification using deep learning. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 2981-2984). IEEE. doi: 10.1109/EMBC46164.2021.9631065
[14] Ibtehaz, N., & Rahman, M. S. (2020). MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural networks, 121, 74-87. doi: 10.1016/j.neunet.2019.08.025
[15] Iglovikov, V., & Shvets, A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746. [Online]. Available: http://arxiv.org/abs/1801.05746
[16] Cheng, D., & Lam, E. Y. (2021). Transfer learning U-Net deep learning for lung ultrasound segmentation. arXiv preprint arXiv:2110.02196. [Online]. Available: http://arxiv.org/abs/2110.02196
[17] Pravitasari, A. A., Iriawan, N., Almuhayar, M., Azmi, T., Irhamah, I., Fithriasari, K., ... & Ferriastuti, W. (2020). UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(3), 1310-1318. doi: 10.12928/TELKOMNIKA.v18i3.14753
[18] Cui, B., Chen, X., & Lu, Y. (2020). Semantic segmentation of remote sensing images using transfer learning and deep convolutional neural network with dense connection. IEEE Access, 8, 116744-116755. doi: 10.1109/ACCESS.2020.3003914
[19] Branch, M. V., & Carvalho, A. S. (2021). Polyp Segmentation in Colonoscopy Images using U-Net-MobileNetV2. arXiv preprint arXiv:2103.15715.
[20] 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. doi: 10.1109/TMI.2018.2845918
[21] Wei, Z., Song, H., Chen, L., Li, Q., & Han, G. (2019). Attention-based DenseUnet network with adversarial training for skin lesion segmentation. IEEE Access, 7, 136616-136629. oi: 10.1109/ACCESS.2019.2940794
[22] Wang, W., Taft, D. A., Chen, Y. J., Zhang, J., Wallace, C. T., Xu, M., ... & Xing, J. (2019). Learn to segment single cells with deep distance estimator and deep cell detector. Computers in biology and medicine, 108, 133-141. doi: 10.1016/j.compbiomed.2019.04.006
[23] Riana, D., Rahayu, S., Hadianti, S., Frieyadie, F., Hasan, M., Karimah, I. N., & Pratama, R. (2022). Identifikasi Citra Pap Smear RepoMedUNM dengan Menggunakan K-Means Clustering dan GLCM. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(1), 1-8. doi: 10.29207/resti.v6i1.3495
[24] Riana, D., Hadianti, S., Rahayu, S., Hasan, M., Karimah, I. N., & Pratama, R. (2021, December). RepoMedUNM: A New Dataset for Feature Extraction and Training of Deep Learning Network for Classification of Pap Smear Images. In International Conference on Neural Information Processing (pp. 317-325). Springer, Cham. doi: 10.1007/978-3-030-92307-5_37
[25] Zhang, J., Hu, Z., Han, G., & He, X. (2016). Segmentation of overlapping cells in cervical smears based on spatial relationship and overlapping translucency light transmission model. Pattern recognition, 60, 286-295. doi: 10.1016/j.patcog.2016.04.021
[26] Lu, Z., Carneiro, G., Bradley, A. P., Ushizima, D., Nosrati, M. S., Bianchi, A. G., ... & Hamarneh, G. (2016). Evaluation of three algorithms for the segmentation of overlapping cervical cells. IEEE journal of biomedical and health informatics, 21(2), 441-450. doi: 10.1109/JBHI.2016.2519686
[27] 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, 9, 82031-82057. doi: 10.1109/ACCESS.2021.3086020
[28] Du, G., Cao, X., Liang, J., Chen, X., & Zhan, Y. (2020). Medical image segmentation based on u-net: A review. Journal of Imaging Science and Technology, 64, 1-12. oi: 10.2352/J.ImagingSci.Technol.2020.64.2.020508
[29] Adiba, A., Hajji, H., & Maatouk, M. (2019, March). Transfer learning and U-Net for buildings segmentation. In Proceedings of the New Challenges in Data Sciences: Acts of the Second Conference of the Moroccan Classification Society (pp. 1-6). doi: 10.1145/3314074.3314088
[30] Karthick, R. (2018). Deep Learning For Age Group Classification System. International Journal Of Advances In Signal And Image Sciences, 4(2), 16-22. doi: 10.29284/ijasis.4.2.2018.16-22
[31] McAuliffe, M. J., Lalonde, F. M., McGarry, D., Gandler, W., Csaky, K., & Trus, B. L. (2001, July). Medical image processing, analysis and visualization in clinical research. In Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001 (pp. 381-386). IEEE. doi: 10.1109/CBMS.2001.941749
[32] Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 658-666). doi: 10.1109/CVPR.2019.00075
[33] Arora, R., Saini, I., & Sood, N. (2021). Multi-label segmentation and detection of COVID-19 abnormalities from chest radiographs using deep learning. Optik, 246, 167780. doi: 10.1016/j.ijleo.2021.167780
[34] Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1-48. doi: 10.1186/s40537-019-0197-0