Skip to main content
SpringerLink
Log in

Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin-enhanced MRCP sequences

  • Research Article
  • Published:
Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objectives

We aimed to develop the first fully automated 3D gallbladder segmentation approach to perform volumetric analysis in volume data of magnetic resonance (MR) cholangiopancreatography (MRCP) sequences. Volumetric gallbladder analysis is performed for non-contrast-enhanced and secretin-enhanced MRCP sequences.

Materials and methods

Native and secretin-enhanced MRCP volume data were produced with a 1.5-T MR system. Images of coronal maximum intensity projections (MIP) are used to automatically compute 2D characteristic shape features of the gallbladder in the MIP images. A gallbladder shape space is generated to derive 3D gallbladder shape features, which are then combined with 2D gallbladder shape features in a support vector machine approach to detect gallbladder regions in MRCP volume data. A region-based level set approach is used for fine segmentation. Volumetric analysis is performed for both sequences to calculate gallbladder volume differences between both sequences.

Results

The approach presented achieves segmentation results with mean Dice coefficients of 0.917 in non-contrast-enhanced sequences and 0.904 in secretin-enhanced sequences.

Conclusion

This is the first approach developed to detect and segment gallbladders in MR-based volume data automatically in both sequences. It can be used to perform gallbladder volume determination in epidemiological studies and to detect abnormal gallbladder volumes or shapes. The positive volume differences between both sequences may indicate the quantity of the pancreatobiliary reflux.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Kamisawa T, Anjiki H, Egawa N, Kurata M, Honda G, Tsuruta K (2008) Diagnosis and clinical implications of pancreatobiliary reflux. World J Gastroenterol 14(43):6622–6626

    Article  PubMed  PubMed Central  Google Scholar 

  2. Motosugi U, Ichikawa T, Araki T (2003) Secretin-stimulating MRCP: the diagnosis of pancreaticobiliary reflux. Nippon Acta Radiol 63(9):591–593

    PubMed  Google Scholar 

  3. Sai JK, Suyama M, Kubokawa Y, Tadokoro H, Sato N, Maehara T, Iida Y, Kojima K (2003) Occult pancreatobiliary reflux in patients with a normal pancreaticobiliary junction. Gastrointest Endosc 57(3):364–368

    Article  PubMed  Google Scholar 

  4. Hosoki T, Hasuike Y, Takeda Y, Michita T, Watanabe Y, Sakamori R, Tokuda Y, Yutani K, Sai C, Mitomo M (2004) Visualization of pancreaticobiliary reflux in anomalous pancreaticobiliary junction by secretin-stimulated dynamic magnetic resonance cholangiopancreatography. Acta Radiol 45(4):375–382

    Article  PubMed  CAS  Google Scholar 

  5. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: 18th international conference on medical image computing and computer-assisted intervention, Springer, Munich (Germany), pp 234–241

  6. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention, Springer, Athens (Greece), pp 424–432

  7. Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: IEEE fourth international conference on 3D vision (3DV), IEEE, pp 565–571

  8. Korez R, Likar B, Pernuš F, Vrtovec T. (2016) Model-based segmentation of vertebral bodies from MR images with 3D CNNs. In: 19th international conference on medical image computing and computer-assisted intervention, Springer, Athens (Greece), pp 433–441

  9. Zhou J, Huang W, Zhang J, Yang T, Liu J, Chui CK, Chang S (2010) Segmentation of gallbladder from CT images for a surgical training system. In: Proceedings of the 3rd IEEE international conference on biomedical engineering and informatics, IEEE, Yantai (China), pp 536–540

  10. Shimizu A, Ohno R, Ikegami T, Kobatake H, Nawano S, Smutek D (2007) Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int J Comput Assist Radiol Surg 2(3–4):135–142

    Article  Google Scholar 

  11. Okada T, Yokota K, Hori M, Nakamato M, Nakamura H, Sato Y (2008) Construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from CT images. In: Proceedings of the 11th conference on medical image computing and computer assisted intervention, Springer, New York, pp 502–509

  12. Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2013) Abdominal multi-organ CT segmentation using organ correlation graph and prediction-based shape and location priors. In: 16th international conference on medical image computing and computer assisted intervention, Springer, pp 275–282

  13. Gauriau R, Ardori R, Lesage D, Bloch I. (2015) Multiple template deformation application to abdominal organ segmentation. In: Proceedings of the 12th international symposium on biomedical imaging, IEEE, New York, pp 359–362

  14. Ciecholewski, M (2010) Gallbladder segmentation in 2-D ultrasound images using deformable contour methods. In: 7th international conference on modeling decisions for artificial intelligence, Springer, Perpignan (France), pp 163–174

  15. Leventon ME, Grimson WEL, Faugeras O (2000) Statistical shape influence in geodesic active contours. In: Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, South Carolina, pp 316–323

  16. Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson WE, Willsk A (2003) A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans Med Imaging 22(2):137–154

    Article  PubMed  Google Scholar 

  17. Tsai A, Wells WM, Warfield SK, Willsky AS (2005) An EM algorithm for shape classification based on level sets. Med Image Anal 9(5):491–502

    Article  PubMed  Google Scholar 

  18. Völzke H, Alte D, Schmidt CO et al. (2010) Cohort profile: the study of health in Pomerania. Intern J Epidemiol 39(4):294–307

    Google Scholar 

  19. Zahn C, Roskies R (1972) Fourier descriptors for plane closed curves. IEEE Trans Comput 21(3):269–281

    Article  Google Scholar 

  20. Folkers A, Samet H (2002) Content-based image retrieval using Fourier descriptors on a logo database. In: Proceedings of the 16th international conference on pattern recognition, IEEE, Quebec City, pp 521–524

  21. Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, London

    Google Scholar 

  22. Chan TF, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  PubMed  CAS  Google Scholar 

  23. Platt J (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif 10(3):61–74

    Google Scholar 

Download references

Acknowledgements

This work was funded by the German Research Foundation under grant number GL 785/1-1. Study of Health in Pomerania is part of the Research Network of Community Medicine at the Ernst Moritz Arndt University of Greifswald, which is funded by the German state of Mecklenburg–West Pomerania.

Author information

Authors and Affiliations

  1. Institute for Community Medicine, Ernst Moritz Arndt University of Greifswald, Walther-Rathenau-Str. 48, 17475, Greifswald, Germany

    Oliver Gloger & Henry Völzke

  2. Institute for Diagnostic Radiology and Neuroradiology, Ernst Moritz Arndt University of Greifswald, Ferdinand-Sauerbruch-Str. 1, 17475, Greifswald, Germany

    Robin Bülow

  3. Department of Simulation and Graphics, Otto von Guericke University of Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Klaus Tönnies

Authors
  1. Oliver Gloger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robin Bülow
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Klaus Tönnies
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Henry Völzke
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

OG developed, implemented, and tested the gallbladder segmentation framework presented, including all modules. He managed the manuscript development and wrote most parts of the manuscript. RB performed manual gallbladder segmentations that were used for training and testing of the framework. He gave helpful advice as a radiologist concerning medical and radiological details. He corrected the manuscript concerning the radiological parts. KT assisted in the revision of the manuscript and corrected several drafts. He gave helpful advice for the manuscript and framework development for the improved approach resulting from his expert knowledge of machine learning and medical image analysis. HV assisted in the development of the manuscript, including corrections from the epidemiological point of view. He gave helpful advice for manuscript development as an epidemiologist and as leader of the Study of Health in Pomerania.

Corresponding author

Correspondence to Oliver Gloger.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gloger, O., Bülow, R., Tönnies, K. et al. Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin-enhanced MRCP sequences. Magn Reson Mater Phy 31, 383–397 (2018). https://doi.org/10.1007/s10334-017-0664-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10334-017-0664-6

Keywords

Search

Navigation