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Medical Image Segmentation Using Deep Learning: A Survey

  • Conference paper
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Digital Technologies and Applications (ICDTA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 669))

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Abstract

During the last few years, medical image segmentation using deep learning has become the most active research area in computer vision. Effectively, researchers become more and more interested in this accurate technique that has a direct impact on the decisions made in different medical fields. The deep learning image segmentation success in different areas, including the medical area, enable us to have the best results. The aim of this paper is two folds, firstly, it presents a study about the most important deep learning architectures used in the medical image segmentation such as the Fully Convolutional Network (FCN), the DeepLab Family and the Convolutional networks for biomedical image segmentation (U-Net) and Generative Adversarial Networks (GANs). Secondly, it provides an analysis for each implemented model in these architectures, which allows highlighting the various common challenges between those models and their adopted approaches.

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References

  1. Moussaoui, H., Benslimane, M., El Akkad, N.: A novel brain tumor detection approach based on fuzzy c-means and marker watershed algorithm. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 871–879. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_79

    Chapter  Google Scholar 

  2. Khrissi, L., El Akkad, N., Satori, H., Satori, K.: Image segmentation based on k-means and genetic algorithms. In: Embedded Systems and Artificial Intelligence, pp. 489–497. Springer (2020)

    Google Scholar 

  3. Moussaoui, H., Benslimane, M., El Akkad, N.: Image segmentation approach based on hybridization between k-means and mask r-cnn. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds.) WITS 2020. LNEE, vol. 745, pp. 821–830. Springer, Singapore (2022). https://doi.org/10.1007/978-981-33-6893-4_74

    Chapter  Google Scholar 

  4. Faska, Z., Khrissi, L., Haddouch, K., EL Akkad, N.: A powerful and efficient method of image segmentation based on random forest algorithm. In: International Conference on Digital Technologies and Applications, pp. 893–903, Springer (2021). https://doi.org/10.1007/978-3-030-73882-2_81

  5. Khrissi, L., El Akkad, N., Satori, H., Satori, K.: Clustering method and sine cosine algorithm for image segmentation. Evol. Intel. 15(1), 669–682 (2021). https://doi.org/10.1007/s12065-020-00544-z

    Article  Google Scholar 

  6. Khrissi, L., EL Akkad, N., Satori, H., Satori, K.: A performant clustering approach based on an improved sine cosine algorithm (2022)

    Google Scholar 

  7. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  8. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, vol. 25 (2012)

    Google Scholar 

  9. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  10. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  11. Nie, D., Wang, L., Gao, Y., Shen, D.: Fully convolutional networks for multimodality isointense infant brain image segmentation. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1342–1345. IEEE (2016)

    Google Scholar 

  12. Nie, D., Wang, L., Adeli, E., Lao, C., Lin, W., Shen, D.: 3-d fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE Trans. Cybernetics 49(3), 1123–1136 (2018)

    Article  Google Scholar 

  13. Wang, S., Yi, L., Chen, Q., Meng, Z., Dong, H., He, Z.: Edge-aware fully convolutional network with crf-rnn layer for hippocampus segmentation. In: 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 803–806. IEEE (2019)

    Google Scholar 

  14. Yang, B., Zhang, W.: Fd-fcn: 3d fully dense and fully convolutional network for semantic segmentation of brain anatomy. arXiv preprint arXiv:1907.09194 (2019)

  15. Valverde, J.M., Shatillo, A., De Feo, R., Gröhn, O., Sierra, A., Tohka, J.: Ratlesnetv2: a fully convolutional network for rodent brain lesion segmentation. Front. Neurosci. 14, 610239 (2020)

    Article  Google Scholar 

  16. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062 (2014)

  17. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  19. Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation, arXiv preprint arXiv:1706.05587 (2017)

  20. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49

    Chapter  Google Scholar 

  21. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image Computing and Computer-Assisted Intervention, pp. 234–241, Springer (2015)

    Google Scholar 

  22. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d unet: learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 424–432. Springer (2016)

    Google Scholar 

  23. Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), pp. 565–571. IEEE (2016)

    Google Scholar 

  24. Li, D., Dharmawan, D.A., Ng, B.P., Rahardja, S.: Residual u-net for retinal vessel segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1425–1429. IEEE (2019)

    Google Scholar 

  25. Punn, N.S., Agarwal, S.: Inception u-net architecture for semantic segmentation to identify nuclei in microscopy cell images. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 16(1), 1–15 (2020)

    Google Scholar 

  26. Cai, S., Tian, Y., Lui, H., Zeng, H., Wu, Y., Chen, G.: Dense-unet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quant. Imaging Med. Surg. 10(6), 1275 (2020)

    Article  Google Scholar 

  27. Oktay, O., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

  28. Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3–11. Springer (2018). https://doi.org/10.1007/978-3-030-00889-5_1

  29. Xiao, X., Lian, S., Luo, Z, Li, S.: Weighted res-unet for high-quality retina vessel segmentation. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 327–331. IEEE (2018)

    Google Scholar 

  30. 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)

  31. Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual u-net. IEEE Geosci. Remote Sens. Lett. 15(5), 749–753 (2018)

    Article  Google Scholar 

  32. Novikov, A.A., Lenis, D., Major, D., Hladvka, J., Wimmer, M., Bühler, K.: Fully convolutional architectures for multiclass segmentation in chest radiographs. IEEE Trans. Med. Imaging 37(8), 1865–1876 (2018)

    Article  Google Scholar 

  33. Kolařík, M., Burget, R., Uher, V., Říha, K., Dutta, M.K.: Optimized high resolution 3d dense-u-net network for brain and spine segmentation. Appl. Sci. 9(3), 404 (2019)

    Article  Google Scholar 

  34. Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. arXiv preprint arXiv:1611.08408 (2016)

  35. Xue, Y., Xu, T., Zhang, H., Long, L.R., Huang, X.: Segan: adversarial network with multi-scale l1 loss for medical image segmentation. Neuro Inform. 16(3), 383–392 (2018)

    Google Scholar 

  36. Dai, W., Dong, N., Wang, Z., Liang, X., Zhang, H., Xing, E.P.: SCAN: structure correcting adversarial network for organ segmentation in chest X-Rays. In: Stoyanov, D., et al. DLMIA ML-CDS 2018. LNCS, vol. 11045. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_30

  37. Khosravan, N., Mortazi, A., Wallace, M., Bagci, U.: Pan: projective adversarial network for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 68–76. Springer (2019). https://doi.org/10.1007/978-3-030-32226-7_8

  38. Hussain, Z., Gimenez, F., Yi, D., Rubin, D.: Differential data augmentation techniques for medical imaging classification tasks. In: AMIA Annual Symposium Proceedings, vol. 2017, p. 979, American Medical Informatics Association (2017). https://doi.org/10.1007/978-3-030-32226-7_8

  39. Abdollahi, B., Tomita, N., Hassanpour, S.: Data Augmentation in Training Deep Learning Models for Medical Image Analysis. In: Nanni, L., Brahnam, S., Brattin, R., Ghidoni, S., Jain, L. (eds.) Deep Learners and Deep Learner Descriptors for Medical Applications. Intelligent Systems Reference Library, vol. 186. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-42750-4_6

  40. Chen, C., et al.: Realistic adversarial data augmentation for mr image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 667–677. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_65

  41. Nalepa, J., Marcinkiewicz, M., Kawulok, M.: Data augmentation for brain tumor segmentation: a review. Front. Comput. Neurosci. 13, 83 (2019)

    Article  Google Scholar 

  42. Sivanesan, U., Braga, L.H., Sonnadara, R.R., Dhindsa, K.: Unsupervised medical image segmentation with adversarial networks: From edge diagrams to segmentation maps. arXiv preprint arXiv:1911.05140 (2019)

  43. Aganj, I., Harisinghani, M.G., Weissleder, R., Fischl, B.: Unsupervised medical image segmentation based on the local center of mass. Sci. Rep. 8(1), 1–8 (2018)

    Article  Google Scholar 

  44. Perone, C.S., Ballester, P., Barros, R.C., Cohen-Adad, J.: Unsupervised domain adaptation for medical imaging segmentation with self-ensembling. NeuroImage 194, 1–11 (2019)

    Google Scholar 

  45. Chenm J., Frey, E.C.: Medical image segmentation via unsupervised convolutional neural network. arXiv preprint arXiv:2001.10155 (2020)

  46. Nie, D., Gao, Y., Wang, Li., Shen, D.: Asdnet: attention based semi-supervised deep networks for medical image segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 370–378. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_43

    Chapter  Google Scholar 

  47. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Geiser, T., Christe, A., Mougiakakou, S.: Semantic segmentation of pathological lung tissue with dilated fully convolutional networks. IEEE J. Biomed. Health Inform. 23(2), 714–722 (2018)

    Google Scholar 

  48. Christ, P.F., et al.: Automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks. arXiv preprint arXiv:1702.05970 (2017)

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Correspondence to Abdelwahid Oubaalla .

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Oubaalla, A., El Moubtahij, H., El Akkad, N. (2023). Medical Image Segmentation Using Deep Learning: A Survey. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-031-29860-8_97

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