Abstract
Pulmonary computerized tomography (CT) images with small slice thickness (thin) is very helpful in clinical practice due to its high resolution for precise diagnosis. However, there are still a lot of CT images with large slice thickness (thick) because of the benefits of storage-saving and short taking time. Therefore, it is necessary to build a pipeline to leverage advantages from both thin and thick slices. In this paper, we try to generate thin slices from the thick ones, in order to obtain high quality images with a low storage requirement. Our method is implemented in an encoder-decoder manner with a proposed progressive up-sampling module to exploit enough information for reconstruction. To further lower the difficulty of the task, a multi-stream architecture is established to separately learn the inner- and outer-lung regions. During training, a contrast-aware loss and feature matching loss are designed to capture the appearance of lung markings and reduce the influence of noise. To verify the performance of the proposed method, a total of 880 pairs of CT images with both thin and thick slices are collected. Ablation study demonstrates the effectiveness of each component of our method and higher performance is obtained compared with previous work. Furthermore, three radiologists are required to detect pulmonary nodules in raw thick slices and the generated thin slices independently, the improvement in both sensitivity and precision shows the potential value of the proposed method in clinical applications.
Q. Liu and Z. Zhou—Equal contribution.
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References
Bae, W., Lee, S., Park, G., Park, H., Jung, K.H.: Residual CNN-based image super-resolution for CT slice thickness reduction using paired CT scans: preliminary validation study (2018)
Chen, Y., Shi, F., Christodoulou, A.G., **e, Y., Zhou, Z., Li, D.: Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 91–99. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_11
Chen, Y., **e, Y., Zhou, Z., Shi, F., Christodoulou, A.G., Li, D.: Brain MRI super resolution using 3D deep densely connected neural networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 739–742. IEEE (2018)
Clark, T.J., Flood, T.F., Maximin, S.T., Sachs, P.B.: Lung CT screening reporting and data system speed and accuracy are increased with the use of a semiautomated computer application. J. Am. Coll. Radiol. 12(12), 1301–1306 (2015)
Cristiano, R., Daniela, O., Massimo, B.: Low-dose CT: technique, reading methods and image interpretation. Cancer Imaging Official Publ. Int. Cancer Imaging Soc. 12(3), 548–556 (2012)
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13
Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_25
Ge, R., Yang, G., Xu, C., Chen, Y., Luo, L., Li, S.: Stereo-correlation and noise-distribution aware ResVoxGAN for dense slices reconstruction and noise reduction in thick low-dose CT. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 328–338. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_37
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Gu, J., Lu, H., Zuo, W., Dong, C.: Blind super-resolution with iterative kernel correction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1604–1613 (2019)
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)
Iwano, S., Makino, N., Ikeda, M., Itoh, S., Ishigaki, T.: Solitary pulmonary nodules: optimal slice thickness of high-resolution CT in differentiating malignant from benign. Clin. Imaging 28(5), 322–328 (2004)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Kudo, A., Kitamura, Y., Li, Y., Iizuka, S., Simo-Serra, E.: Virtual thin slice: 3D conditional GAN-based super-resolution for CT slice interval. In: Knoll, F., Maier, A., Rueckert, D., Ye, J.C. (eds.) MLMIR 2019. LNCS, vol. 11905, pp. 91–100. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33843-5_9
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Macmahon, H., et al.: Guidelines for management of incidental pulmonary nodules detected on CT images: from the fleischner society 2017. Radiology 284, 228–243 (2017). 161659
Mahapatra, D., Bozorgtabar, B., Garnavi, R.: Image super-resolution using progressive generative adversarial networks for medical image analysis. Comput. Med. Imaging Graph. 71, 30–39 (2019)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883 (2016)
Silpa, K., Mastani, S.A.: Comparison of image quality metrics. Int. J. Eng. Res. Technol. (IJERT) 1(4), 5 (2012)
Acknowledgment
This work was supported in part by National Key Research and Development Program of China (MOST-2018AAA0102004, 2019YFC0118100), Bei**g Municipal Science and Technology Planning Project (Grant No. Z201100005620008), National Natural Science Foundation of China (NSFC-61625201), and Hong Kong Research Grants Council through Research Impact Fund under Grant R-5001-18.
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Liu, Q., Zhou, Z., Liu, F., Fang, X., Yu, Y., Wang, Y. (2020). Multi-stream Progressive Up-Sampling Network for Dense CT Image Reconstruction. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_50
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