Abstract
Contrast-enhanced T1 (T1ce) is one of the most essential magnetic resonance imaging (MRI) modalities for diagnosing and analyzing brain tumors, especially gliomas. In clinical practice, common MRI modalities such as T1, T2, and fluid attenuation inversion recovery are relatively easy to access while T1ce is more challenging considering the additional cost and potential risk of allergies to the contrast agent. Therefore, it is of great clinical necessity to develop a method to synthesize T1ce from other common modalities. Current paired image translation methods typically have the issue of requiring a large amount of paired data and do not focus on specific regions of interest, e.g., the tumor region, in the synthesization process. To address these issues, we propose a Difficulty-perceived common-to-T1ce Semi-Supervised multimodal MRI Synthesis network (DS\(^3\)-Net), involving both paired and unpaired data together with dual-level knowledge distillation. DS\(^3\)-Net predicts a difficulty map to progressively promote the synthesis task. Specifically, a pixelwise constraint and a patchwise contrastive constraint are guided by the predicted difficulty map. Through extensive experiments on the publicly-available BraTS2020 dataset, DS\(^3\)-Net outperforms its supervised counterpart in each respect. Furthermore, with only 5% paired data, the proposed DS\(^3\)-Net achieves competitive performance with state-of-the-art image translation methods utilizing 100% paired data, delivering an average SSIM of 0.8947 and an average PSNR of 23.60. The source code is available at https://github.com/Huangziqi777/DS-3_Net.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Armanious, K., et al.: MedGAN: medical image translation using GANs. Comput. Med. Imaging Graph. 79, 101684 (2020)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 1–13 (2017)
Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613–2622. (2021)
Dar, S.U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., Çukur, T.: Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging 38(10), 2375–2388 (2019)
Hinton, G., Vinyals. O., Dean. J.: Distilling the knowledge in a neural network. arXiv e-prints (2015). arXiv:1503.02531
Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, pp. 2366–2369 (2010)
Huang, Z., Lin, L., Cheng, P., Peng, L., Tang, X.: Multi-modal brain tumor segmentation via missing modality synthesis and modality-level attention fusion. In: 2022 26th International Conference on Pattern Recognition. (Under review) (2022)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Ibrahim, M.S., Vahdat, A., Ranjbar, M., Macready, W.G.: Semi-supervised semantic image segmentation with self-correcting networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12715–12725 (2020)
Işın, A., Direkoğlu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput. Sci. 102, 317–324 (2016)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Li, C., Wand, M.: Precomputed real-time texture synthesis with Markovian generative adversarial networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 702–716. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_43
Li, H., et al.: DiamondGAN: unified multi-modal generative adversarial networks for MRI sequences synthesis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 795–803. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_87
Liu, S., et al.: Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inform. 2(3), 167–180 (2015). https://doi.org/10.1007/s40708-015-0019-x
Loshchilov, I., Hutter, F.: Fixing Weight decay regularization in Adam. arXiv e-prints (2017). arxiv:1711.05101
Ma, B., et al.: MRI image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hippocampal subfields segmentation. Comput. Med. Imaging Graph. 86, 101800 (2020)
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)
Van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv e-prints (2018). arXiv:1807.03748
Park, T., Efros, A.A., Zhang, R., Zhu, J.-Y.: Contrastive learning for unpaired image-to-image translation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 319–345. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_19
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
Sharma, A., Hamarneh, G.: Missing MRI pulse sequence synthesis using multi-modal generative adversarial network. IEEE Trans. Med. Imaging 39(4), 1170–1183 (2019)
Shen, Y., Gao, M.: Brain tumor segmentation on MRI with missing modalities. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 417–428. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_32
Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 1–28 (2015)
Wu, J., Tang, X.: Brain segmentation based on multi-atlas and diffeomorphism guided 3D fully convolutional network ensembles. Pattern Recogn. 115, 107904 (2021)
Yang, J., et al.: Fast t2w/flair MRI acquisition by optimal sampling of information complementary to pre-acquired t1w MRI, arXiv e-prints (2021). arXiv:2111.06400
Yurt, M., Dar, S.U., Erdem, A., Erdem, E., Oguz, K.K., Çukur, T.: mustGAN: multi-stream generative adversarial networks for MR image synthesis. Med. Image Anal. 70, 101944 (2021)
Zhang, Y., Wu, J., Liu, Y., Chen, Y., Wu, E.X., Tang, X.: Mi-UNet: multi-inputs UNet incorporating brain parcellation for stroke lesion segmentation from t1-weighted magnetic resonance images. IEEE J. Biomed. Health Inform. 25(2), 526–535 (2021)
Zhou, T., Canu, S., Vera, P., Ruan, S.: Brain tumor segmentation with missing modalities via latent multi-source correlation representation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 533–541. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_52
Zhou, T., Canu, S., Vera, P., Ruan, S.: Latent correlation representation learning for brain tumor segmentation with missing MRI modalities. IEEE Trans. Image Process. 30, 4263–4274 (2021)
Zhou, Y., Chen, H., Lin, H., Heng, P.-A.: Deep semi-supervised knowledge distillation for overlapping cervical cell instance segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 521–531. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_51
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)
Acknowledgement
This study was supported by the National Natural Science Foundation of China (62071210); the Shenzhen Science and Technology Program (RCYX20210609103056042); the Shenzhen Basic Research Program (JCYJ20200925153847004, JCYJ20190809120205578).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, Z., Lin, L., Cheng, P., Pan, K., Tang, X. (2022). DS\(^3\)-Net: Difficulty-Perceived Common-to-T1ce Semi-supervised Multimodal MRI Synthesis Network. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13436. Springer, Cham. https://doi.org/10.1007/978-3-031-16446-0_54
Download citation
DOI: https://doi.org/10.1007/978-3-031-16446-0_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16445-3
Online ISBN: 978-3-031-16446-0
eBook Packages: Computer ScienceComputer Science (R0)