Abstract
Paired multi-modality medical images, can provide complementary information to help physicians make more reasonable decisions than single modality medical images. But they are difficult to generate due to multiple factors in practice (e.g., time, cost, radiation dose). To address these problems, multi-modality medical image translation has aroused increasing research interest recently. However, the existing works mainly focus on translation effect of a whole image instead of a critical target area or Region of Interest (ROI), e.g., organ and so on. This leads to poor-quality translation of the localized target area which becomes blurry, deformed or even with extra unreasonable textures. In this paper, we propose a novel target-aware generative adversarial network called TarGAN, which is a generic multi-modality medical image translation model capable of (1) learning multi-modality medical image translation without relying on paired data, (2) enhancing quality of target area generation with the help of target area labels. The generator of TarGAN jointly learns mapping at two levels simultaneously—whole image translation mapping and target area translation mapping. These two mappings are interrelated through a proposed crossing loss. The experiments on both quantitative measures and qualitative evaluations demonstrate that TarGAN outperforms the state-of-the-art methods in all cases. Subsequent segmentation task is conducted to demonstrate effectiveness of synthetic images generated by TarGAN in a real-world application. Our code is available at https://github.com/cs-xiao/TarGAN.
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References
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789–8797 (2018)
Ernst, P., Hille, G., Hansen, C., Tönnies, K., Rak, M.: A CNN-based framework for statistical assessment of spinal shape and curvature in whole-body MRI images of large populations. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 3–11. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_1
Fu, C., et al.: Three dimensional fluorescence microscopy image synthesis and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2221–2229 (2018)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANS. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)
Gupta, L., Klinkhammer, B.M., Boor, P., Merhof, D., Gadermayr, M.: GAN-based image enrichment in digital pathology boosts segmentation accuracy. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 631–639. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_70
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANS trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems, pp. 6626–6637 (2017)
Huang, P.U., et al.: CoCa-GAN: common-feature-learning-based context-aware generative adversarial network for glioma grading. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 155–163. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_18
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: NNU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Meth. 18(2), 203–211 (2021)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANS for improved quality, stability, and variation. In: International Conference on Learning Representations (2018)
Kavur, A.E., et al.: Chaos challenge-combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. 69, 101950 (2021)
Kavur, A.E., Selver, M.A., Dicle, O., Barıs, M., Gezer, N.S.: Chaos-combined (CT-MR) healthy abdominal organ segmentation challenge data. In: Proceedings of IEEE International Symposium Biomedical Image (ISBI) (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (Poster) (2015)
Martin Arjovsky, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia (2017)
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
Shen, L., et al.: Multi-domain image completion for random missing input data. IEEE Trans. Med. Imaging 40(4), 1113–1122 (2020)
Xin, B., Hu, Y., Zheng, Y., Liao, H.: Multi-modality generative adversarial networks with tumor consistency loss for brain MR image synthesis. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1803–1807. IEEE (2020)
Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., Bourgeat, P.: EA-GANS: edge-aware generative adversarial networks for cross-modality MR image synthesis. IEEE Trans. Med. Imaging 38(7), 1750–1762 (2019)
Zhang, Z., Yang, L., Zheng, Y.: Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9242–9251 (2018)
Zhu, D., et al.: UGAN: Untraceable GAN for multi-domain face translation. arXiv preprint arXiv:1907.11418 (2019)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Acknowledgments
This work is supported in part by the Natural Science Foundation of Guangdong Province (2017A030313358, 2017A030313355, 2020A15 15010717), the Guangzhou Science and Technology Planning Project (201704030051), the Fundamental Research Funds for the Central Universities (2019MS073), NSF-1850492 (to R.L.) and NSF-2045804 (to R.L.).
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Chen, J., Wei, J., Li, R. (2021). TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12906. Springer, Cham. https://doi.org/10.1007/978-3-030-87231-1_3
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