Skip to main content
Log in

A method for liver segmentation in perfusion MR images using probabilistic atlases and viscous reconstruction

  • Short Paper
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Magnetic resonance (MR) tomographic images are routinely used in diagnosis of liver pathologies. Liver segmentation is needed for these types of images. It is therefore an important requirement for later tasks such as comparison among studies of different patients, as well as studies of the same patient (including those taken during the diffusion of a contrast, as in perfusion MR imaging). However, automatic segmentation of the liver is a challenging task due to certain reasons such as the high variability of liver shapes, similar intensity values and unclear contours between the liver and surrounding organs, especially in perfusion MR images. In order to overcome these limitations, this work proposes the use of a probabilistic atlas for liver segmentation in perfusion MR images, and the combination of the information gathered with that provided by level-based segmentation methods. The process starts with an under-segmented shape that grows slice by slice using morphological techniques (namely, viscous reconstruction); the result of the closest segmented slice and the probabilistic information provided by the atlas. Experiments with a collection of manually segmented liver images are provided, including numerical evaluation using widely accepted metrics for shape comparison.

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

Access this article

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

Instant access to the full article PDF.

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

References

  1. Baddeley A, Molchanov I (1998) Averaging of random sets based on their distance functions. J Math Imaging Vis 8:79–92

    Article  MathSciNet  Google Scholar 

  2. Ben-Cohen A, Diamant I, Klang E, Amitai M, Greenspan H (2016) Fully convolutional network for liver segmentation and lesions detection. In: Carneiro G et al (eds) Deep learning and data labeling for medical applications: first international workshop, LABELS 2016, and second international workshop, DLMIA 2016, held in conjunction with MICCAI 2016 (Athens, Greece. October 2016). Springer, New York, pp 77–85. https://doi.org/10.1007/978-3-319-46976-8_9

    Chapter  Google Scholar 

  3. Cabezas M, Oliver A, Llad X, Freixenet J, Cuadra MB (2011) A review of atlas-based segmentation for magnetic resonance brain images. Comput Methods Programs Biomed 104(3):e158–e177. https://doi.org/10.1016/j.cmpb.2011.07.015

    Article  Google Scholar 

  4. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell PAMI 8(6):679–698

    Article  Google Scholar 

  5. Chartrand G, Cresson T, Chav R, Gotra A, Tang A, de Guise JA (2017) Liver segmentation on CT and MR using laplacian mesh optimization. IEEE Trans Biomed Eng 99:1. https://doi.org/10.1109/TBME.2016.2631139

    Article  Google Scholar 

  6. Chen G, Gu L, Qian L, Xu J (2009) An improved level set for liver segmentation and perfusion analysis in MRIs. IEEE Trans Inf Technol Biomed 13(1):94–103. https://doi.org/10.1109/TITB.2008.2007110

    Article  Google Scholar 

  7. Christ PF, Ettlinger F, Grün F, et al (2017) Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. Comput Res Repos CoRR abs/1702.05970. http://arxiv.org/abs/1702.05970

  8. Dima T, Domingo J, Dura E (2011) A local level set method for liver segmentation in functional MR imaging. In: Nuclear science symposium and medical imaging conference (NSS/MIC 2011). IEEE, pp 3158–3161. https://doi.org/10.1109/NSSMIC.2011.6152575

  9. Dongxiang C, Tiankun L (2009) Iterative quadtree decomposition segmentation of liver MR image. In: AICI ’09. international conference on artificial intelligence and computational intelligence, vol 3, pp 527–529. https://doi.org/10.1109/AICI.2009.152

  10. Esfandiarkhani M, Foruzan AH (2017) A generalized active shape model for segmentation of liver in low-contrast CT volumes. Comput Biol Med 82:59–70. https://doi.org/10.1016/j.compbiomed.2017.01.009, http://www.sciencedirect.com/science/article/pii/S0010482517300100

    Article  Google Scholar 

  11. Göçeri E (2016) Fully automated liver segmentation using Sobolev gradient-based level set evolution. Int J Numer Methods Biomed Eng 32(11):e0,2765–n/a. https://doi.org/10.1002/cnm.2765,e02765 CNM-Jun-15-0095.R1

  12. Göçeri E, Unlu MZ, Guzelis C, Dicle O (2012) An automatic level set based liver segmentation from MRI data sets. In: 2012 3rd international conference on image processing theory, tools and applications (IPTA), pp 192–197. https://doi.org/10.1109/IPTA.2012.6469551

  13. Göçeri E, Görcan MN, Dicle O (2014) Fully automated liver segmentation from SPIR image series. Comput Biol Med 53:265–278

    Article  Google Scholar 

  14. Göçeri E, Unlu M, Dicle O (2015) A comparative performance evaluation of various approaches for liver segmentation from SPIR images. Turk J Electr Eng Comput Sci. https://doi.org/10.3906/elk-1304-36

    Article  Google Scholar 

  15. Hanbury A, Kammerer P, Zolda E (2003) Painting crack elimination using viscous morphological reconstruction. In: Proceedings of 12th international conference on image analysis and processing, pp 226–231

  16. Hardisty M, Gordon L, Agarwal P, Skrinskas T, Whyne C (2007) Quantitative characterization of metastatic disease in the spine. Part I: semiautomated segmentation using atlas-based deformable registration and the level set method. Med Phys 34(8):3127–3134. https://doi.org/10.1118/1.2746498

    Article  Google Scholar 

  17. Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PMM, Chi Y, Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer HP, Nemeth G, Raicu DS, Rau AM, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265. https://doi.org/10.1109/TMI.2009.2013851

    Article  Google Scholar 

  18. Isgum I (2009) Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in CT scans. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2008.2011480

    Article  Google Scholar 

  19. Ji H, He J, Yang X, Deklerck R, Cornelis J (2013) ACM-based automatic liver segmentation from 3D CT images by combining multiple atlases and improved mean-shift techniques. IEEE J Biomed Health Inf 17(3):690–698. https://doi.org/10.1109/JBHI.2013.2242480

    Article  Google Scholar 

  20. Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang MC, Christensen GE, Collins DL, Gee J, Hellier P, Song JH, Jenkinson M, Lepage C, Rueckert D, Thompson P, Vercauteren T, Woods RP, Mann JJ, Parsey RV (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3):786–802. https://doi.org/10.1016/j.neuroimage.2008.12.037, http://www.sciencedirect.com/science/article/pii/S1053811908012974

    Article  Google Scholar 

  21. Larrey-Ruiz J, Morales-Sánchez J, Bastida-Jumilla MC, Menchón-Lara RM, Verdú-Monedero R (2014) Automatic image-based segmentation of the heart from CT scans. EURASIP J Image Video Process 2014(1):1–13. https://doi.org/10.1186/1687-5281-2014-52

    Article  Google Scholar 

  22. Li G, Chen X, Shi F, Zhu W, Tian J, Xiang D (2015) Automatic liver segmentation based on shape constraints and deformable graph cut in CT images. IEEE Trans Image Process 24(12):5315–5329. https://doi.org/10.1109/TIP.2015.2481326

    Article  MathSciNet  Google Scholar 

  23. Liao M, Zhao YQ, Liu XY, Zeng YZ, Zou BJ, Wang XF, Shih FY (2017) Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. Comput Methods Programs Biomed 143:1–12. https://doi.org/10.1016/j.cmpb.2017.02.015, http://www.sciencedirect.com/science/article/pii/S0169260716304059

    Article  Google Scholar 

  24. Linguraru MG, Sandberg JK, Li Z, Shah F, Summers RM (2010) Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation. Med Phys 37(2):771–783

    Article  Google Scholar 

  25. Loader C (2013) locfit: local regression, likelihood and density estimation, no 5–9, vol 1. R package version, p 1. https://cran.r-project.org/package=locfit

  26. Lu F, Wu F, Hu P, Peng Z, Kong D (2017) Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 12(2):171–182. https://doi.org/10.1007/s11548-016-1467-3

    Article  Google Scholar 

  27. Masoumi H, Behrad A, Pourmina MA, Roosta A (2012) Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network. Biomed Signal Process Control 7(5):429–437. https://doi.org/10.1016/j.bspc.2012.01.002

    Article  Google Scholar 

  28. Mharib A, Ramli AR, Mashohor S, Mahmood RB (2012) Survey on liver CT image segmentation methods. Artif Intell Rev 37(2):83–95. https://doi.org/10.1007/s10462-011-9220-3

    Article  Google Scholar 

  29. Michopoulou SK, Costaridou L, Panagiotopoulos E, Speller R, Panayiotakis G, Todd-Pokropek A (2009) Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. IEEE Trans Biomed Eng 56(9):2225–2231. https://doi.org/10.1109/TBME.2009.2019765

    Article  Google Scholar 

  30. Mostafa A, Hassanien AE, Houseni M, Hefny H (2017) Liver segmentation in MRI images based on whale optimization algorithm. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-4638-5

    Article  Google Scholar 

  31. Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22(4):483–492. https://doi.org/10.1109/TMI.2003.809139

    Article  Google Scholar 

  32. Park H, Hero A, Bland P, Kessler M, Seo J, Meyer C (2010) Construction of abdominal probabilistic atlases and their value in segmentation of normal organs in abdominal CT scans. IEICE Trans Inf Syst E93–D(8):2291–2301

    Article  Google Scholar 

  33. Peng J, Wang Y, Kong D (2014) Liver segmentation with constrained convex variational model. Pattern Recognit Lett 43:81–88. https://doi.org/10.1016/j.patrec.2013.07.010, http://www.sciencedirect.com/science/article/pii/S0167865513002766, iCPR2012 Awarded Papers

    Article  Google Scholar 

  34. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639

    Article  Google Scholar 

  35. Pohl KM, Fisher J, Bouix S, Shenton M, McCarley RW, Grimson WEL, Kikinis R, Wells WM (2007) Using the logarithm of odds to define a vector space on probabilistic atlases. Med Image Anal 11(5):465–477. https://doi.org/10.1016/j.media.2007.06.003 special Issue on the Ninth International Conference on Medical Image Computing and Computer-Assisted Interventions - {MICCAI} 2006

    Article  Google Scholar 

  36. Stoyan D, Stoyan H (1994) Fractals, random shapes and point fields. Methods of geometrical statistics. Wiley, Chichester

    MATH  Google Scholar 

  37. Sun C, Guoa S, Zhangb H, Lib J, Chanc M, Maa S, Jina L, Liua X, Lia X, Qian X (2017) Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs. Artif Intell Med (to appear). https://doi.org/10.1016/j.artmed.2017.03.008, http://www.sciencedirect.com/science/article/pii/S0933365716305930

    Article  Google Scholar 

  38. Zheng Y, Ai D, Mu J, Cong W, Wang X, Zhao H, Yang J (2017) Automatic liver segmentation based on appearance and context information. BioMed Eng OnLine 16(1):16. https://doi.org/10.1186/s12938-016-0296-5

    Article  Google Scholar 

  39. Zhou X, Kitagawa T, Okuo K, Hara T, Fujita H, Yokoyama R, Kanematsu M, Hoshi H (2005) Construction of a probabilistic atlas for automated liver segmentation in non-contrast torso CT images. Int Congr Ser 1281:1169–1174. https://doi.org/10.1016/j.ics.2005.03.079

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by Project DPI2013-45742-R from the Spanish Ministry of Economy and Competitiveness with FEDER funds.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Esther Dura.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dura, E., Domingo, J., Göçeri, E. et al. A method for liver segmentation in perfusion MR images using probabilistic atlases and viscous reconstruction. Pattern Anal Applic 21, 1083–1095 (2018). https://doi.org/10.1007/s10044-017-0666-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-017-0666-z

Keywords

Navigation