Abstract
Dual-energy (DE) chest radiographs provide greater diagnostic information than standard radiographs by separating the image into bone and soft tissue, revealing suspicious lesions which may otherwise be obstructed from view. However, acquisition of DE images requires two physical scans, necessitating specialized hardware and processing, and images are prone to motion artifact. Generation of virtual DE images from standard, single-shot chest radiographs would expand the diagnostic value of standard radiographs without changing the acquisition procedure. We present a Multi-scale Conditional Adversarial Network (MCA-Net) which produces high-resolution virtual DE bone images from standard, single-shot chest radiographs. Our proposed MCA-Net is trained using the adversarial network so that it learns sharp details for the production of high-quality bone images. Then, the virtual DE soft tissue image is generated by processing the standard radiograph with the virtual bone image using a cross projection transformation. Experimental results from 210 patient DE chest radiographs demonstrated that the algorithm can produce high-quality virtual DE chest radiographs. Important structures were preserved, such as coronary calcium in bone images and lung lesions in soft tissue images. The average structure similarity index and the peak signal to noise ratio of the produced bone images in testing data were 96.4 and 41.5, which are significantly better than results from previous methods. Furthermore, our clinical evaluation results performed on the publicly available dataset indicates the clinical values of our algorithms. Thus, our algorithm can produce high-quality DE images that are potentially useful for radiologists, computer-aided diagnostics, and other diagnostic tasks.
B. Zhou and X. Lin—Equally contribute to this work.
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
Kelcz, F., Zink, F., Peppler, W., Kruger, D., Ergun, D., Mistretta, C.: Conventional chest radiography vs dual-energy computed radiography in the detection and characterization of pulmonary nodules. AJR Am. J. Roentgenol. 162, 271–278 (1994)
Li, F., Hara, T., Shiraishi, J., Engelmann, R., MacMahon, H., Doi, K.: Improved detection of subtle lung nodules by use of chest radiographs with bone suppression imaging: receiver operating characteristic analysis with and without localization. Am. J. Roentgenol. 196, W535–W541 (2011)
Zhou, B., et al.: Detection and quantification of coronary calcium from dual energy chest x-rays: phantom feasibility study. Med. Phys. 44, 5106–5119 (2016)
Zhou, B., Jiang, Y., Wen, D., Gilkeson, R.C., Hou, J., Wilson, D.L.: Visualization of coronary artery calcium in dual energy chest radiography using automatic rib suppression. In: Medical Imaging 2018: Image Processing, vol. 10574, p. 105740E. International Society for Optics and Photonics (2018)
Wen, D., et al.: Enhanced coronary calcium visualization and detection from dual energy chest x-rays with sliding organ registration. Comput. Med. Imaging Graph. 64, 12–21 (2018)
Chen, S., Suzuki, K.: Computerized detection of lung nodules by means of “virtual dual-energy” radiography. IEEE Trans. Biomed. Eng. 60, 369–378 (2013)
Vock, P., Szucs-Farkas, Z.: Dual energy subtraction: principles and clinical applications. Eur. J. Radiol. 72, 231–237 (2009)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3462–3471. IEEE (2017)
Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35, 1285–1298 (2016)
Roth, H.R., et al.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_68
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint (2017)
Agrawal, A., Raskar, R., Chellappa, R.: Edge suppression by gradient field transformation using cross-projection tensors. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2301–2308. IEEE (2006)
Suzuki, K., Abe, H., Li, F., Doi, K.: Suppression of the contrast of ribs in chest radiographs by means of massive training artificial neural network. In: Medical Imaging 2004: Image Processing, vol. 5370, pp. 1109–1120. International Society for Optics and Photonics (2004)
Suzuki, K., Abe, H., MacMahon, H., Doi, K.: Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans. Med. Imaging 25, 406–416 (2006)
Chen, S., Suzuki, K.: Separation of bones from chest radiographs by means of anatomically specific multiple massive-training anns combined with total variation minimization smoothing. IEEE Trans. Med. Imaging 33, 246–257 (2014)
Chen, S., Zhong, S., Yao, L., Shang, Y., Suzuki, K.: Enhancement of chest radiographs obtained in the intensive care unit through bone suppression and consistent processing. Phys. Med. Biol. 61, 2283 (2016)
Loog, M., van Ginneken, B., Schilham, A.M.: Filter learning: application to suppression of bony structures from chest radiographs. Med. Image Anal. 10, 826–840 (2006)
Simkó, G., Orbán, G., Máday, P., Horváth, G.: Elimination of clavicle shadows to help automatic lung nodule detection on chest radiographs. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds.) 4th European Conference of the International Federation for Medical and Biological Engineering. IFMBE, vol. 22, pp. 488–491. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-89208-3_116
Hogeweg, L., Sanchez, C.I., van Ginneken, B.: Suppression of translucent elongated structures: applications in chest radiography. IEEE Trans. Med. Imaging 32, 2099–2113 (2013)
Rasheed, T., Ahmed, B., Khan, M.A., Bettayeb, M., Lee, S., Kim, T.S.: Rib suppression in frontal chest radiographs: a blind source separation approach. In: 9th International Symposium on Signal Processing and Its Applications, ISSPA 2007, pp. 1–4. IEEE (2007)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436 (2015)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint (2016)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality. Stability, and Variation. arXiv preprint (2017)
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
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Schilham, A.M., Van Ginneken, B., Loog, M.: A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database. Med. Image Anal. 10, 247–258 (2006)
Chakraborty, D., Yoon, H.J., Mello-Thoms, C.: Spatial localization accuracy of radiologists in free-response studies: inferring perceptual froc curves from mark-rating data. Acad. Radiol. 14, 4–18 (2007)
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
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, B., Lin, X., Eck, B., Hou, J., Wilson, D. (2019). Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs Using Multi-scale and Conditional Adversarial Network. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-20887-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20886-8
Online ISBN: 978-3-030-20887-5
eBook Packages: Computer ScienceComputer Science (R0)