Abstract
Prostate magnetic resonance imaging (MRI) offers accurate details of structures and tumors for prostate cancer brachytherapy. However, it is unsuitable for routine treatment since MR images differ significantly from trans-rectal ultrasound (TRUS) images conventionally used for radioactive seed implants in brachytherapy. TRUS imaging is fast, convenient, and widely available in the operation room but is known for its low soft-tissue contrast and tumor visualization capability in the prostate area. Conventionally, practitioners usually rely on prostate segmentation to fuse the two imaging modalities with non-rigid registration. However, prostate delineation is often not available on diagnostic MR images. Besides, the high non-linear intensity relationship between two imaging modalities poses a challenge to non-rigid registration. Hence, we propose a method to generate a TRUS-styled image from a prostate MR image to replace the role of the TRUS image in radiation therapy dose pre-planning. We propose a structural constraint to handle non-linear projections of anatomical structures between MR and TRUS images. We further include an adversarial mechanism to enforce the model to preserve anatomical features in an MR image (such as prostate boundary and dominant intraprostatic lesion (DIL)) while synthesizing the TRUS-styled counterpart image. The proposed method is compared with other state-of-art methods with real TRUS images as the reference. The results demonstrate that the TRUS images synthesized by our method can be used for brachytherapy treatment planning for prostate cancer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lemaître, G., Martí, R., Freixenet, J., Vilanova, J.C., Walker, P.M., Meriaudeau, F.: Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput. Biol. Med. 60, 8–31 (2015). https://doi.org/10.1016/j.compbiomed.2015.02.009. https://www.sciencedirect.com/science/article/pii/S001048251500058X
Bloch, N., Madabhushi, A., Huisman, H., et al.: NCI-ISBI 2013 challenge: automated segmentation of prostate structures (2015)
Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning (Adaptive Computation and Machine Learning). The MIT Press, Cambridge (2006)
Chen, R., Huang, W., Huang, B., Sun, F., Fang, B.: Reusing discriminators for encoding: towards unsupervised image-to-image translation (2020)
Ghahramani, Z.: Unsupervised learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) ML -2003. LNCS (LNAI), vol. 3176, pp. 72–112. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28650-9_5
Goodfellow, I.J., et al.: Generative adversarial networks (2014)
Han, J., Shoeiby, M., Petersson, L., Armin, M.A.: Dual contrastive learning for unsupervised image-to-image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)
Huang, X., Liu, M.Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: ECCV (2018)
Jiao, J., Namburete, A.I.L., Papageorghiou, A.T., Noble, J.A.: Self-supervised ultrasound to MRI fetal brain image synthesis (2020)
Kang, T., Lee, K.H.: Unsupervised image-to-image translation with self-attention networks. In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), February 2020. https://doi.org/10.1109/bigcomp48618.2020.00-92
Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the Sobel operator. IEEE J. Solid-State Circuits 23(2), 358–367 (1988)
Lee, H.Y., Tseng, H.Y., Huang, J.B., Singh, M.K., Yang, M.H.: Diverse image-to-image translation via disentangled representations. In: European Conference on Computer Vision (2018)
Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks (2018)
Ma, Z., Collins, M.: Noise contrastive estimation and negative sampling for conditional models: consistency and statistical efficiency (2018). https://doi.org/10.48550/ARXIV.1809.01812. https://arxiv.org/abs/1809.01812
Modat, M., et al.: Fast free-form deformation using graphics processing units. Comput. Methods Programs Biomed. 98, 278–84 (2009). https://doi.org/10.1016/j.cmpb.2009.09.002
Morris, W., et al.: Population-based study of biochemical and survival outcomes after permanent 125I brachytherapy for low- and intermediate-risk prostate cancer. Urology 73(4), 860–865 (2009). https://doi.org/10.1016/j.urology.2008.07.064
Onofrey, J.A., Oksuz, I., Sarkar, S., Venkataraman, R., Staib, L.H., Papademetris, X.: MRI-TRUS image synthesis with application to image-guided prostate intervention. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2016. LNCS, vol. 9968, pp. 157–166. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46630-9_16
Prada, P., et al.: Long-term outcomes in patients younger than 60 years of age treated with brachytherapy for prostate cancer. Strahlentherapie und Onkologie 194, 311–317 (2018). https://doi.org/10.1007/s00066-017-1238-2
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation (2015)
**e, G., Wang, J., Huang, Y., Zheng, Y., Zheng, F., **, Y.: A survey of cross-modality brain image synthesis (2022)
Yang, H., et al.: Unsupervised MR-to-CT synthesis using structure-constrained CycleGAN. IEEE Trans. Med. Imaging 39(12), 4249–4261 (2020). https://doi.org/10.1109/TMI.2020.3015379
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks (2020)
Acknowledgements
This work was supported in part by NIH grant CA206100. Yunzhi Huang was in part supported by the National Natural Science Foundation of China under Grant No. 62101365 and the startup foundation of Nan**g University of Information Science and Technology.
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
Pang, Y., Chen, X., Huang, Y., Yap, PT., Lian, J. (2022). Weakly Supervised MR-TRUS Image Synthesis for Brachytherapy of Prostate Cancer. 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_46
Download citation
DOI: https://doi.org/10.1007/978-3-031-16446-0_46
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)
