Abstract
This paper investigates the problem of pseudo-healthy synthesis that is defined as synthesizing a subject-specific pathology-free image from a pathological one. Recent approaches based on Generative Adversarial Network (GAN) have been developed for this task. However, these methods will inevitably fall into the trade-off between preserving the subject-specific identity and generating healthy-like appearances. To overcome this challenge, we propose a novel adversarial training regime, Generator versus Segmentor (GVS), to alleviate this trade-off by a divide-and-conquer strategy. We further consider the deteriorating generalization performance of the segmentor throughout the training and develop a pixel-wise weighted loss by muting the well-transformed pixels to promote it. Moreover, we propose a new metric to measure how healthy the synthetic images look. The qualitative and quantitative experiments on the public dataset BraTS demonstrate that the proposed method outperforms the existing methods. Besides, we also certify the effectiveness of our method on datasets LiTS. Our implementation and pre-trained networks are publicly available at https://github.com/Au3C2/Generator-Versus-Segmentor.
Y. Zhang and C. Li—Equal contribution.
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References
Andermatt, S., Horváth, A., Pezold, S., Cattin, P.: Pathology segmentation using distributional differences to images of healthy origin. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 228–238. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11723-8_23
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Baumgartner, C.F., Koch, L.M., Can Tezcan, K., Xi Ang, J., Konukoglu, E.: Visual feature attribution using Wasserstein GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8309–8319 (2018)
Bilic, P., et al.: The liver tumor segmentation benchmark (LiTs). arXiv preprint arXiv:1901.04056 (2019)
Bowles, C., et al.: Pseudo-healthy image synthesis for white matter lesion segmentation. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2016. LNCS, vol. 9968, pp. 87–96. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46630-9_9
Bowles, C., et al.: Brain lesion segmentation through image synthesis and outlier detection. NeuroImage Clin. 16, 643–658 (2017)
Chen, X., Konukoglu, E.: Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. arXiv preprint arXiv:1806.04972 (2018)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
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
Sato, D., et al.: A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes. In: Medical Imaging 2018: Computer-Aided Diagnosis, vol. 10575, p. 105751P. International Society for Optics and Photonics (2018)
Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)
Sun, L., Wang, J., Huang, Y., Ding, X., Greenspan, H., Paisley, J.: An adversarial learning approach to medical image synthesis for lesion detection. IEEE J. Biomed. Health Inf. (2020)
Tsunoda, Y., Moribe, M., Orii, H., Kawano, H., Maeda, H.: Pseudo-normal image synthesis from chest radiograph database for lung nodule detection. In: Kim, Y.S., Ryoo, Y.J., Jang, M., Bae, Y.-C. (eds.) Advanced Intelligent Systems. AISC, vol. 268, pp. 147–155. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05500-8_14
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, vol. 2, pp. 1398–1402. IEEE (2003)
Xia, T., Chartsias, A., Tsaftaris, S.A.: Pseudo-healthy synthesis with pathology disentanglement and adversarial learning. Med. Image Anal. 64, 101719 (2020)
Ye, D.H., Zikic, D., Glocker, B., Criminisi, A., Konukoglu, E.: Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 606–613. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40811-3_76
Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016)
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 was supported in part by National Key Research and Development Program of China (No. 2019YFC0118101), in part by National Natural Science Foundation of China under Grants U19B2031, 61971369, in part by Fundamental Research Funds for the Central Universities 20720200003, in part by the Science and Technology Key Project of Fujian Province, China (No. 2019HZ020009).
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Zhang, Y. et al. (2021). Generator Versus Segmentor: Pseudo-healthy Synthesis. 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_15
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DOI: https://doi.org/10.1007/978-3-030-87231-1_15
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