Abstract
CT arterial phase images can provide a powerful auxiliary to formulate pancreatic neoplasm diagnosis and treatment plans. In the absence of such images, we can use the image translation model convert CT images of other phases into CT arterial phase synthetic images. Under the supervision of manual labeling by experts or pixel-level labeling, the model can achieve better performance. However, for pancreatic neoplasm image translation, such labels are usually scarce. In this regard, we use the easily obtained paired but unaligned cross-phase real pancreatic neoplasm images as labels and constructs a cross-phase image feature correlation analysis-based image translation method (CFCA-IT). This method analyzes the image feature correlation between the synthetic images and real images and takes it as the training constraint of the translation model. Simulation experiments show that CFCA-IT can further improve the translation performance of the five state-of-the-art translation models.
This work is supported in part by the National Natural Science Foundation of China (61802347, 61972347, 61773348, U20A20171), and the Natural Science Foundation of Zhejiang Province (LGF20H180002, LY21F020027, LSD19H180003).
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Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE International Conference on Computer Vision (ICCV), pp. 2242–2251 (2017)
Cai, J., Zhang, Z., Cui, L., Zheng, Y., Yang, L.: Towards cross-modal organ translation and segmentation: a cycle-and shape-consistent generative adversarial network. Med. Image Anal. 52, 174–184 (2019)
Wang, C.J., Rost, N.S., Golland, P.: Spatial-intensity transform GANs for high fidelity medical image-to-image translation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 749–759. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59713-9_72
Xing, F., Bennett, T., Ghosh, D.: Adversarial domain adaptation and pseudo-labeling for cross-modality microscopy image quantification. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 740–749. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_82
Chartsias, A., Joyce, T., Giuffrida, M.V., Tsaftaris, S.A.: Multimodal MR synthesis via modality-invariant latent representation. IEEE Trans. Med. Imaging 37, 803–814 (2018)
Zhou, T., Fu, H., Chen, G., Shen, J., Shao, L.: Hi-Net: hybrid-fusion network for multi-modal MR image synthesis. IEEE Trans. Med. Imaging 39, 2772–2781 (2020)
Shen, L., et al.: Multi-Domain image completion for random missing input data. IEEE Trans. Med. Imaging 40, 1113–1122 (2021)
Wang, M., Li, P.: A review of deformation models in medical image registration. J. Med. Biol. Eng. 39, 1C17 (2019)
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16, 2639–2664 (2004)
Goodfellow, I.J., et al.: Generative adversarial nets. In: Neural Information Processing System, vol. 2, p. 2672C2680 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179–196. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_11
Yang, H., et al.: Unsupervised MR-to-CT synthesis using structure-constrained CycleGAN. In: IEEE Transactions on Medical Imaging, vol. 39, pp. 4249–4261 (2020)
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Chen, Y., Wei, Z., Yang, XH., Li, Z., Guan, Q., Chen, F. (2021). Pancreatic Neoplasm Image Translation Based on Feature Correlation Analysis of Cross-Phase Image. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_32
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DOI: https://doi.org/10.1007/978-3-030-92310-5_32
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