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Automatic 3D liver location and segmentation via convolutional neural network and graph cut

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans.

Methods

The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map.

Results

The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively.

Conclusions

The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.

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Notes

  1. In detail, they are the livers 02, 04, 06, 08, 10, 12, 14, 16, 18 and 20.

  2. In detail, they are the livers 01, 03, 05, 07, 09, 11, 13, 15, 17 and 19.

References

  1. Afifi A, Nakaguchi T (2012) Liver segmentation approach using graph cuts and iteratively estimated shape and intensity constrains. Int Conf Med Image Comput Comput Assist Interv 15:395–403

    Google Scholar 

  2. Al-Shaikhli S, Yang M, Rosenhahn B (2015) 3D automatic liver segmentation using feature-constrained mahalanobis distance in CT images. Biomed Tech Biomed Eng. doi:10.1515/bmt-2015-0017

    Google Scholar 

  3. Al-Shaikhli S, Yang M, Rosenhahn B (2015) Automatic 3D liver segmentation using sparse representation of global and local image information via level set formulation. http://arxiv.org/abs/1508.01521

  4. Beichel R, Bornik A, Bauer C, Sorantin E (2012) Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods. Med Phys 39(3):1361–1373

    Article  PubMed  PubMed Central  Google Scholar 

  5. Bland J, Altman D (2010) Statistical methods for assessing agreement between two methods of clinical measurement. Int J Nurs Stud 47:931–936

    Article  Google Scholar 

  6. Boykov Y, Funka-Lea G (2006) Graph cuts and efficient N-D image segmentation. Int J Comput Vision 70(2):109–131

    Article  Google Scholar 

  7. Boykov Y, Jolly M (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: Proceedings 8th IEEE international conference on computer vision. ICCV 2001. IEEE 1, pp 105–112

  8. Cernazanu-Glavan C, Holban S (2013) Segmentation of bone structure in X-ray images using convolutional neural network. Adv Electr Comput Eng 13(1):87–94

    Article  Google Scholar 

  9. Chen X, Bagci U (2011) 3D automatic anatomy segmentation based on iterative graph-cut-asm. Med Phys 38(8):4610–4622

    Article  PubMed  PubMed Central  Google Scholar 

  10. Chung F, Delingette H (2013) Regional appearance modeling based on the clustering of intensity profiles. Comput Vis Image Underst 117(6):705–717

    Article  Google Scholar 

  11. Dan CC, Giusti A, Gambardella LM (2012) Schmidhuber: deep neural networks segment neuronal membranes in electron microscopy images. Nips 4:2843–2851

  12. Erdt M, Steger S, Kirschner M, Wesarg S (2010) Fast automatic liver segmentation combining learned shape priors with observed shape deviation. In: Proceedings of the 26th IEEE international symposium on computer-based medical systems, pp 249–254

  13. Freiman M, Eliassaf O, Taieb Y, Joskowicz L, Azraq Y, Sosna J (2008) An iterative Bayesian approach for nearly automatic liver segmentation: algorithm and validation. Int J Comput Assist Radiol Surg 3(5):439–446

    Article  Google Scholar 

  14. Gauriau R, Cuingnet R, Prevost R, Mory B, Ardon R, Lesage D, Bloch I (2013) A generic, robust and fully-automatic workflow for 3D CT liver segmentation. Springer, Berlin

    Book  Google Scholar 

  15. Heimann T, Meinzer HP (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13(4):543–563

    Article  PubMed  Google Scholar 

  16. Heimann T, Meinzer HP, Wolf I (2007) A statistical deformable model for the segmentation of liver CT volumes. In: Miccai workshop on 3D segmentation in the clinic, pp 161–166

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

    Article  PubMed  Google Scholar 

  18. Huang C, Jia F, Li Y, Zhang X, Luo H, Fang C, Fan Y (2012) Automatic liver segmentation based on shape constrained differeomorphic demons atlas registration. In: International conference on electronics, communications and control, pp 126–129

  19. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D (2011) Global cancer statistics. CA Cancer J Clin 61(2):69–90

    Article  PubMed  Google Scholar 

  20. Kainmüller D, Lange T, Lamecker H (2007) Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: Proceeding MICCAI workshop 3-D segmentat. Clinic: a gand challenge, pp 109–116

  21. Kinda A, Saddi Rousson M, Hotel CC, Cheriet F (2007) Global to local shape matching for liver segmentation in CT imaging. In: Miccai workshop on 3D segmentation in the clinic, pp 207–214

  22. Kirschner M (2013) The probabilistic active shape model: from model construction to flexible medical image segmentation. Ph.D. dissertation

  23. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25(2):2012

    Google Scholar 

  24. Lécun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  25. Lee J, Kim N, Lee H, Seo JB, Won HJ, Shin YM, Shin YG, Kim SH (2007) Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images. Comput Methods Programs Biomed 88(1):26–38

    Article  PubMed  Google Scholar 

  26. Li G, Chen X, Shi F, Zhu W, Tian J (2015) Automatic liver segmentation based on shape constraints and deformable graph cut in CT images. IEEE Trans Image Process 24(12):5315–5329

    Article  PubMed  Google Scholar 

  27. Linguraru MG, Richbourg WJ, Watt JM, Pamulapati V, Summers RM (2011) Liver and tumor segmentation and analysis from CT of diseased patients via a generic affine invariant shape parameterization and graph cuts. In: International conference on abdominal imaging: computational and clinical applications, pp 198–206

  28. Massoptier L, Casciaro S (2007) Fully automatic liver segmentation through graph-cut technique. In: 29th annual international conference of the IEEE engineering in medicine and biology society 2007, pp 5243–5246

  29. Ni K, Bresson X, Chan T, Esedoglu S (2007) Local histogram based segmentation using the wasserstein distance. In: Scale space and variational methods in computer vision, first international conference, pp 97–111

  30. Pan S, Dawant BM (2006) Automatic 3D segmentation of the liver from abdominal CT images: a level-set approach. Proc SPIE 4322:128–138

    Article  Google Scholar 

  31. Park H, Bland P, Meyer C (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22(4):483–492

    Article  PubMed  Google Scholar 

  32. Peng J, Dong F, Chen Y, Kong D (2014) A region appearance based adaptive variational model for 3D liver segmentation. Med Phys 41(4):043502

    Article  PubMed  Google Scholar 

  33. Peng J, Wang Y, Kong D (2014) Liver segmentation with constrained convex variational model. Pattern Recognit Lett 43:81–88

    Article  Google Scholar 

  34. Peng J, Hu P, Lu F, Peng Z, Kong D, Zhang H (2015) 3D liver segmentation using multiple region appearances and graph cuts. Med Phys 42(12):6840–6852

    Article  PubMed  Google Scholar 

  35. Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M (2013) Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: International conference on medical image computing and computer-assisted intervention, pp 246–253

  36. Ruskó L, Bekes G, Fidrich M (2009) Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images. Med Image Anal 13(6):871–882

    Article  PubMed  Google Scholar 

  37. Shi C, Cheng Y, Liu F, Wang Y, Bai J, Tamura S (2015) A hierarchical local region-based sparse shape composition for liver segmentation in CT scans. Pattern Recognit 50(C):88–106

    Google Scholar 

  38. Szegedy C, Toshev A, Erhan D (2013) Deep neural networks for object detection. Adv Neural Inf Process Syst 1:2555–2563

  39. Tomoshige S, Oost E, Shimizu A, Watanabe H, Nawano S (2014) A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast CT images. Med Image Anal 18(1):130–143

    Article  PubMed  Google Scholar 

  40. Wang G, Zhang S, Li F, Gu L (2013) A new segmentation framework based on sparse shape composition in liver surgery planning system. Med Phys 40(5):792–796

    Article  Google Scholar 

  41. Wang J, Cheng Y, Guo C, Wang Y, Tamura S (2016) Shape–intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. Int J Comput Assist Radiol Surg 11:817–826

  42. Weickert J, Romeny BMTH, Viergever MA (1998) Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans Image Process 7(3):398–410

    Article  CAS  PubMed  Google Scholar 

  43. Wimmer A, Hornegger J, Soza G (2009) Implicit active shape model employing boundary classifier. In: 19th international conference on pattern recognition, 2008. ICPR 2008, pp 1–4

  44. Wimmer A, Soza G, Hornegger J (2009) A generic probabilistic active shape model for organ segmentation. Lect Notes Comput Sci 12:26–33

    Google Scholar 

  45. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp 818–833

  46. Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage 108:214–224

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The authors would like to thank Dr. Jing Yuan and Dr. Jialin Peng for their valuable discussions and useful suggestions. This work was supported in part by National Natural Science Foundation of China (Grant Nos. 11271323, 91330105, 11401231) and the Zhejiang Provincial Natural Science Foundation of China (Grant No.: LZ13A010002).

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    Authors and Affiliations

    1. School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China

      Fang Lu, Fa Wu, Peijun Hu & Dexing Kong

    2. Department of Radiology, First Affiliated Hospital of Zhejiang University, Hangzhou, 310003, China

      Zhiyi Peng

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    1. Fang Lu
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    2. Fa Wu
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    Correspondence to Dexing Kong.

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    This article does not contain any studies with human participants or animals performed by any of the authors.

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

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    Fang Lu and Fa Wu have contributed equally to this work.

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    Lu, F., Wu, F., Hu, P. et al. Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J CARS 12, 171–182 (2017). https://doi.org/10.1007/s11548-016-1467-3

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