Advertisement

Automatic Slice Identification in 3D Medical Images with a ConvNet Regressor

  • Bob D. de Vos
  • Max A. Viergever
  • Pim A. de Jong
  • Ivana Išgum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10008)

Abstract

Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor.

In 150 chest CT scans two reference slices were manually identified: one containing the aortic root and another superior to the aortic arch. In two independent experiments, the ConvNet regressor was trained with 100 CTs to determine the distance between each slice and the SOI in a CT. To identify the SOI, a first order polynomial was fitted through the obtained distances.

In 50 test scans, the mean distances between the reference and the automatically identified slices were 5.7 mm (4.0 slices) for the aortic root and 5.6 mm (3.7 slices) for the aortic arch.

The method shows similar results for both tasks and could be used for automatic slice identification.

Keywords

Slice identification Localization Detection Convolutional neural network Regression Deep learning 

Notes

Acknowledgments

This study was funded by the Netherlands Organization for Scientific Research (NWO)/Foundation for Technology Sciences (STW); Project 12726.

The authors thank the National Cancer Institute for access to NCI’s data collected by the National Lung Screening Trial. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI.

The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.

References

  1. 1.
    The National Lung Screening Trial: Overview and study design. Radiology 258(1),243–253 (2011)Google Scholar
  2. 2.
    Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. arXiv preprint arXiv:1405.3531 (2014)
  3. 3.
    Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv:1511.07289 [cs], November 2015
  4. 4.
    Criminisi, A., Robertson, D., Konukoglu, E., Shotton, J., Pathak, S., White, S., Siddiqui, K.: Regression forests for efficient anatomy detection and localization in computed tomography scans. Med. Image Anal. 17(8), 1293–1303 (2013)CrossRefGoogle Scholar
  5. 5.
    Han, D., Gao, Y., Wu, G., Yap, P.T., Shen, D.: Robust anatomical landmark detection with application to MR brain image registration. Comput. Med. Imaging Graph. 46, 277–290 (2015). Part 3CrossRefGoogle Scholar
  6. 6.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. JMLR 37 (2015)Google Scholar
  7. 7.
    Kingma, D., Ba, J.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs], December 2014
  8. 8.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  9. 9.
    Liu, D., Zhou, S.K.: Anatomical landmark detection using nearest neighbor matching and submodular optimization. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 393–401. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Malietzis, G., Aziz, O., Bagnall, N., Johns, N., Fearon, K., Jenkins, J.: The role of body composition evaluation by computerized tomography in determining colorectal cancer treatment outcomes: a systematic review. Eur. J. Surg. Oncol. (EJSO) 41(2), 186–196 (2015)CrossRefGoogle Scholar
  11. 11.
    Zheng, Y., Georgescu, B., Comaniciu, D.: Marginal space learning for efficient detection of 2D/3D anatomical structures in medical images. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 411–422. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Bob D. de Vos
    • 1
  • Max A. Viergever
    • 1
  • Pim A. de Jong
    • 2
  • Ivana Išgum
    • 1
  1. 1.Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
  2. 2.Department of RadiologyUniversity Medical Center UtrechtUtrechtThe Netherlands

Personalised recommendations