Abstract
Automated lesion segmentation from computed tomography (CT) is an important and challenging task in medical image analysis. While many advancements have been made, there is room for continued improvements. One hurdle is that CT images can exhibit high noise and low contrast, particularly in lower dosages. To address this, we focus on a preprocessing method for CT images that uses stacked generative adversarial networks (SGAN) approach. The first GAN reduces the noise in the CT image and the second GAN generates a higher resolution image with enhanced boundaries and high contrast. To make up for the absence of high quality CT images, we detail how to synthesize a large number of low- and high-quality natural images and use transfer learning with progressively larger amounts of CT images. We apply both the classic GrabCut method and the modern holistically nested network (HNN) to lesion segmentation, testing whether SGAN can yield improved lesion segmentation. Experimental results on the DeepLesion dataset demonstrate that the SGAN enhancements alone can push GrabCut performance over HNN trained on original images. We also demonstrate that HNN + SGAN performs best compared against four other enhancement methods, including when using only a single GAN.
Y. Tang and J. Cai—Equal contribution.
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
References
Tang, Y., Harrison, A.P., et al.: Semi-automatic recist labeling on ct scans with cascaded convolutional neural networks. arXiv:1806.09507 (2018)
Jin, D., Xu, Z., et al.: Ct-realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation. arXiv:1806.04051 (2018)
Tang, Y., Wang, X., et al.: Attention-guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs. arXiv:1807.07532 (2018)
Cai, J., Tang, Y., et al.: Accurate weakly-supervised deep lesion segmentation using large-scale clinical annotations: Slice-propagated 3d mask generation from 2d recist. arXiv:1807.01172 (2018)
Massoptier, L., Casciaro, S.: A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from ct scans. Eur. Radiol. 18(8), 1658 (2008)
Christ, P.F., Elshaer, M.E.A., et al.: Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields. MICCA I, 415–423 (2016)
Dabov, K., Foi, A.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE TIP 16(8), 2080–2095 (2007)
Zhang, K., Zuo, W., et al.: Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE TIP 26(7), 3142–3155 (2017)
Goodfellow, I., Pouget-Abadie, J., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)
Ledig, C., Theis, L., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR, pp. 4681–4690 (2017)
Yan, K., Wang, X., et al.: Deep lesion graphs in the wild: relationship learning and organization of significant radiology image findings in a diverse large-scale lesion database. In: CVPR, pp. 9261–9270 (2018)
Shi, W., Caballero, J., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR, pp. 1874–1883 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: ICML, pp. 448–456 (2015)
He, K., Zhang, X., et al.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: ICCV, pp. 1026–1034 (2015)
Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: Dataset and study. In: CVPRW, pp. 1122–1131 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
Deng, J., Dong, W., et al.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)
Rother, C., Kolmogorov, V., et al.: Grabcut: interactive foreground extraction using iterated graph cuts. In: ACM TOG, pp. 309–314 (2004)
Xie, S., Tu, Z.: Holistically-nested edge detection. In: ICCV, pp. 1395–1403 (2015)
Acknowledgments
This research was supported by the Intramural Research Program of the National Institutes of Health Clinical Center and by the Ping An Insurance Company through a Cooperative Research and Development Agreement. We thank Nvidia for GPU card donation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply
About this paper
Cite this paper
Tang, Y. et al. (2018). CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-00919-9_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00918-2
Online ISBN: 978-3-030-00919-9
eBook Packages: Computer ScienceComputer Science (R0)