Abstract
Segmentation of coronary arteries in X-ray angiograms is a crucial step in the assessment of coronary disease. Recently, many automatic approaches have been proposed to minimize time-consuming clinicians intervention. However, due to noise and complex vessel structure in this modality, most of those approaches fail to segment thin vessels. In this paper, we introduce a new generative adversarial network called Res-GAN to obtain accurate vessel segmentation of both thick and thin vessels. It consists of a Residual-UNet generator following the encoder-decoder structure; and a Residual CNN discriminator for more efficient segmentation. Besides, in order to improve the training process, we adopt a loss function combining both binary cross-entropy and Dice losses. For the experiment results, we used our private dataset to compare the proposed architecture with others state-of-the-art models. The results demonstrate that Res-GAN outperforms the others architectures. It achieves the highest accuracy of 96,55% and Dice metric of 81,18%.
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References
Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., Asari, V.K.: Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955 (2018)
Banerjee, A., Galassi, F., Zacur, E., De Maria, G.L., Choudhury, R.P., Grau, V.: Point-cloud method for automated 3d coronary tree reconstruction from multiple non-simultaneous angiographic projections. IEEE Trans. Med. Imaging 39(4), 1278–1290 (2019)
Jun, T.J., Kweon, J., Kim, Y.H., Kim, D.: T-net: encoder-decoder in encoder-decoder architecture for the main vessel segmentation in coronary angiography. arXiv preprint arXiv:1905.04197 (2019)
Kamran, S.A., Hossain, K.F., Tavakkoli, A., Zuckerbrod, S.L., Sanders, K.M., Baker, S.A.: RV-GAN: retinal vessel segmentation from fundus images using multi-scale generative adversarial networks. arXiv preprint arXiv:2101.00535 (2021)
Kerkeni, A., Benabdallah, A., Manzanera, A., Bedoui, M.H.: A coronary artery segmentation method based on multiscale analysis and region growing. Comput. Med. Imaging Graph. 48, 49–61 (2016)
Moccia, S., De Momi, E., El Hadji, S., Mattos, L.S.: Blood vessel segmentation algorithms-review of methods, datasets and evaluation metrics. Comput. Methods Programs Biomed. 158, 71–91 (2018)
Mou, L., et al.: Cs2-net: deep learning segmentation of curvilinear structures in medical imaging. Med. Image Anal. 67, 101874 (2021)
Popescu, D., Deaconu, M., Ichim, L., Stamatescu, G.: Retinal blood vessel segmentation using pix2pix gan
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shi, X., Du, T., Chen, S., Zhang, H., Guan, C., Xu, B.: Uenet: a novel generative adversarial network for angiography image segmentation. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1612–1615. IEEE (2020)
Son, J., Park, S.J., Jung, K.H.: Retinal vessel segmentation in fundoscopic images with generative adversarial networks. arXiv preprint arXiv:1706.09318 (2017)
Son, J., Park, S.J., Jung, K.H.: Towards accurate segmentation of retinal vessels and the optic disc in fundoscopic images with generative adversarial networks. J. Digit. Imaging 32(3), 499–512 (2019)
Wu, C., Zou, Y., Yang, Z.: U-GAN: generative adversarial networks with u-net for retinal vessel segmentation. In: 2019 14th International Conference on Computer Science & Education (ICCSE), pp. 642–646. IEEE (2019)
Acknowledgement
This work was supported by the Ministry of Higher Education and Scientific Research of Tunisia through the PEJC Young Researchers Encouragement Program (Project code 20PEJC 05-16).
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Hamdi, R., Kerkeni, A., Bedoui, M.H., Ben Abdallah, A. (2022). Res-GAN: Residual Generative Adversarial Network for Coronary Artery Segmentation. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_38
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DOI: https://doi.org/10.1007/978-3-031-21753-1_38
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