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A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab

  • S.I. : Brain inspired Computing &Machine Learning Applied Research-BISMLARE
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Abstract

Proper liver segmentation is a key step in many clinical applications, including computer-assisted diagnosis, radiation therapy and volume measurement. However, liver segmentation is still challenging due to fuzzy boundary, complex liver anatomy, present of pathologies, and diversified shape. This paper presents a novel two-stage liver detection and segmentation model DSL. The first stage uses improved Faster Regions with CNN features (Faster R-CNN) to detect approximate position of liver. The obtained images are processed and input into DeepLab to obtain the contour of liver. The proposed approach is validated on two datasets MICCAI-Sliver07 and 3Dircadb. Experimental results reveal that the proposed method outperforms the state-of-the-art solutions in terms of volume overlap error, average surface distance, relative volume difference, and total score.

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References

  1. Alshaikhli SDS, Yang MY, Rosenhahn B (2015) Automatic 3D liver segmentation using sparse representation of global and local image information via level set formulation. arXiv:1508.01521

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

    Google Scholar 

  3. Burkhardt H (2010) Integration of morphology and graph-based techniques for fully automatic liver segmentation. Majlesi J Electr Eng 4(3):59–66

    Google Scholar 

  4. Campadelli P, Casiraghi E (2009) Liver segmentation from CT scans: a survey. Artif Intell Med 45(2):185–196

    Google Scholar 

  5. Chen LC, Papandreou G, Kokkinos I et al (2014) Semantic image segmentation with deep convolutional nets and fully connected CRFs. Comput Sci 4:357–361

    Google Scholar 

  6. Chen LC, Papandreou G, Kokkinos I et al (2017) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Google Scholar 

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

    Google Scholar 

  8. Dawant BM, Li R, Lennon B et al (2007) Semi-automatic segmentation of the liver and its evaluation on the MICCAI 2007 grand challenge data set. In: Proceedings of MICCAI workshop 3D segmentation clinic: a grand challenge, pp 215–221

  9. Dong C, Chen YW, Tateyama T et al (2016) A knowledge-based interactive liver segmentation using random walks. In: International conference on fuzzy systems and knowledge discovery. IEEE, pp 1731–1736

  10. Dou Q, Chen H, Jin Y et al (2016) 3D Deeply supervised network for automatic liver segmentation from CT volumes, pp 149–157

  11. Erdt M, Steger S, Kirschner M et al (2010) Fast automatic liver segmentation combining learned shape priors with observed shape deviation. In: IEEE, international symposium on computer-based medical systems. IEEE, pp 249–254

  12. Gambino O, Vitabile S, Re GL et al (2010) Automatic volumetric liver segmentation using texture based region growing. In: International conference on complex, intelligent and software intensive systems. IEEE Computer Society, pp 146–152

  13. Ginneken BV, Heimann T, Styner M (2007) 3D Segmentation in the clinic: a grand challenge. In: Proceedings of 3D segmentation in the clinic: a grand challenge. Springer, Brisbane, pp 7–15

  14. Girshick R (2015) Fast R-CNN. In: IEEE international conference on computer vision. IEEE, pp 1440–1448

  15. Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE international conference on computer vision and pattern recognition, pp 580–587

  16. He B, Huang C, Sharp G et al (2016) Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model. Med Phys 43(5):2421

    Google Scholar 

  17. Heimann T, Meinzer HP, Wolf I (2010) A statistical deformable model for the segmentation of liver CT volumes. In: MICCAI workshop on 3D Segmentation in the Clinic

  18. Heimann T, Van GB, Styner MA et al (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251–1265

    Google Scholar 

  19. Jansen J, Schreurs R, Dubois L et al (2015) Orbital volume analysis: validation of a semi-automatic software segmentation method. Int J Comput Assist Radiol Surg 11(1):11–18

    Google Scholar 

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

  21. Saddi KA, Rousson M et al (2007) Global-to-local shape matching for liver segmentation in CT imaging

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

  23. Krähenbühl P, Koltun V (2011) Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Proceedings of advances in neural information processing systems, vol 24, pp 109–117

  24. Liao M, Zhao YQ, Wang W et al (2016) Efficient liver segmentation in CT images based on graph cuts and bottleneck detection. Phys Med 32(11):1383

    Google Scholar 

  25. Li G, Chen X, Shi F et al (2015) Automatic liver segmentation based on shape constraints and deformable graph cut in CT Images. IEEE Trans Image Process 24(12):5315

    MathSciNet  MATH  Google Scholar 

  26. Linguraru MG, Richbourg WJ, Watt JM et al (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. Springer, Berlin, pp 198–206

    Google Scholar 

  27. Lu F, Wu F, Hu P et al (2017) Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 12(2):171–182

    Google Scholar 

  28. Lu J, Shi L, Deng M et al (2011) An interactive approach to liver segmentation in CT based on deformable model integrated with attractor force. In: International conference on machine learning and cybernetics. IEEE, pp 1660–1665

  29. Njg W (2017) Alternative RNA splicing in the pathogenesis of liver disease. Front Endocrinol 8:133

    Google Scholar 

  30. Ren S, He K, Girshick R et al (2015) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137

    Google Scholar 

  31. Schmidt G, Athelogou MA, Schönmeyer R et al (2007) Cognition network technology for a fully automated 3-D segmentation of liver. In: MICCAI workshop on 3D Segmentation in the clinic: a grand challenge

  32. Wang N, Huang L L, Zhang B (2010) A fast hybrid method for interactive liver segmentation. In: Chinese conference on pattern recognition, pp 1–5

  33. Wimmer A, Soza G, Hornegger J (2009) A generic probabilistic active shape model for organ segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2009. Springer, Berlin, pp 26–33

    Google Scholar 

  34. Yan J, Schwartz LH, Zhao B (2015) Semiautomatic segmentation of liver metastases on volumetric CT images. Med Phys 42(11):6283–6293

    Google Scholar 

  35. Yang D, Xu D, Zhou S K et al (2017) Automatic liver segmentation using an adversarial image-to-image network

  36. Yang X, Yu HC, Choi Y et al (2014) A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points. Comput Methods Programs Biomed 113(1):69–79

    Google Scholar 

  37. Shelhamer E, Long J, Darrell T (2014) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651

    Google Scholar 

  38. Shrivastava A, Gupta A, Girshick R (2016) Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 761–769

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Acknowledgements

This work was partly supported by the National Nature Science Foundation of China (No. 61309013 and No. 51608070) and Chongqing Basic and frontier research projects (No. CSTC2014JCYJA40042 and No. CSTC2016JCYJA0022).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, No.174 ShaZheng Street, ShaPingBa District, Chongqing, China

    Wei Tang, Dongsheng Zou, Su Yang, Jing Shi & Jingpei Dan

  2. Sichuan Changhong Elextronics Co. Ltd., No.35 EastMianyangRoad, High-Tech, Mianyang, Sichuan, China

    Guowu Song

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  1. Wei Tang
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  2. Dongsheng Zou
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  3. Su Yang
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  4. Jing Shi
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  5. Jingpei Dan
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  6. Guowu Song
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Correspondence to Dongsheng Zou.

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Tang, W., Zou, D., Yang, S. et al. A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab. Neural Comput & Applic 32, 6769–6778 (2020). https://doi.org/10.1007/s00521-019-04700-0

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  • DOI: https://doi.org/10.1007/s00521-019-04700-0

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