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Automatic Extraction of Liver Region from Medical Images by Using an MFUnet

  • Vi, Vo Thi Tuong (Dept. of AI Convergence, Chonnam National University) ;
  • Oh, A-Ran (Dept. of AI Convergence, Chonnam National University) ;
  • Lee, Guee-Sang (Dept. of AI Convergence, Chonnam National University) ;
  • Yang, Hyung-Jeong (Dept. of AI Convergence, Chonnam National University) ;
  • Kim, Soo-Hyung (Dept. of AI Convergence, Chonnam National University)
  • Received : 2020.02.13
  • Accepted : 2020.09.15
  • Published : 2020.09.30

Abstract

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.

Keywords

References

  1. E. Shelhamer, J. Long, and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 640-651, 2017 https://doi.org/10.1109/TPAMI.2016.2572683
  2. O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9351, pp. 234-241, 2015
  3. T. Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017 Janua, pp. 936-944, 2017
  4. S. J. Kim and H. S. Kim, "Multi Tasking U-net 기반 파프리카 병해충 진단." Smart Media J., vol. 9, no. 1, pp. 16-22, 2020 https://doi.org/10.30693/SMJ.2020.9.1.16
  5. M. Langkvist, L. Karlsson, and A. Loutfi, "Inception-v4, Inception ResNet and the Impact of Residual Connections on Learning," Pattern Recognit. Lett., vol. 42, no. 1, pp. 11-24, 2014 https://doi.org/10.1016/j.patrec.2014.01.008
  6. M. Scully, V. Magnotta, C. Gasparovic, P. Pelligrimo, D. Feis, and H. J. Bockholt, "3D Segmentation In The Clinic: A Grand Challenge II at MICCAI 2008 - MS Lesion Segmentation," Miccai, pp. 1-6, 2008
  7. X. Zhuang, "Challenges and methodologies of fully automatic whole heart segmentation: A review," J. Healthc. Eng., vol. 4, no. 3, pp. 371-407, 2013 https://doi.org/10.1260/2040-2295.4.3.371
  8. G. Litjens et al., "Evaluation of prostate segmentation algorithms for MRI: the PROMISE12," Med Image Anal, vol. 18, no. 2, pp. 359-373, 2015 https://doi.org/10.1016/j.media.2013.12.002
  9. O. A. J. Del Toro et al., "VISCERAL -VISual concept extraction challenge in RAdioLogy: ISBI 2014 challenge organization," CEUR Workshop Proc., vol. 1194, pp. 6-15, 2014
  10. A. Basher, S. Ahmed, and H. Y. Jung, "One Step Measurements of hippocampal Pure Volumes from MRI Data Using an Ensemble Model of 3-D Convolutional Neural Network," Smart Media J., vol. 9, no. 2, pp. 22-32, 2020 https://doi.org/10.30693/smj.2020.9.2.22
  11. N. C. F. Codella et al., "Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)," Proc. Int. Symp. Biomed. Imaging, vol. 2018 April, pp. 168-172, 2018
  12. B. van Ginneken, T. Heimann, and M. Styner, "3-D segmentation in the clinic: A grand challeng," Int. Conf. Med. Image Comput. Comput. Assist. Interv., vol. 10, pp. 7-15, 2007
  13. Y. Boykov et al., "Liver Segmentation in CT Data : A Segmentation Refinement Approach," Methods, vol. 70, no. c, pp. 109-131, 2007
  14. Y. Boykov and G. Funka Lea, "Graph cuts and efficient N-D image segmentation," Int. J. Comput. Vis., vol. 70, no. 2, pp. 109-131, 2006 https://doi.org/10.1007/s11263-006-7934-5
  15. W. A. Barrett and E. N. Mortensen, "In teractive live-wire boundary extraction," Med. Image Anal., vol. 1, no. 4, pp. 331-341, 1997 https://doi.org/10.1016/S1361-8415(97)85005-0
  16. A. Beck and V. Aurich, "Hepatux - a semiautomatic liver segmentation system," 3D Segmentation Clin. A Gd. Chall., pp. 225-233, 2007
  17. B. M. Dawant, R. Li, B. Lennon, and S. Li, "Semi-automatic segmentation of the liver and its evaluation on the MICCAI 2007 grand challenge data set," 3D Segmentation Clin. A Gd. Chall., pp. 215-221, 2007
  18. A. Wimmer, G. Soza, and J. Hornegger, "Two stage semi-automatic organ segmentation framework using radial basis functions and level sets," 3D segmentation Clin. a Gd. challenge, LNCS, Springer Berlin, Heidelb., pp. 207-214, 2007
  19. P. Slagmolen, A. Elen, D. Seghers, D. Loeckx, F. Maes, and K. Haustermans, "Atlas based liver segmentation usi ng nonrigid registration with a B-spline transformation model," Proc. MICCAI Work. 3D segmentation Clin. a Gd. Chall. Chall., pp. 197-206, 2007
  20. H. Lamecker, T. Lange, and M. Seebass, "Segmentation of the liver using a 3D statistical shape model," Konrad-Zus- Zent rum fur Informationstechnik Berlin, vol. 09, no. April, pp. 27, 2004
  21. D. Kainmueller, T. Lange, and H. Lamecker, "Shape constrained automatic segmentation of the liver based on a heuristic intensity model," Proc. MICCAI Work. 3D Segmentation Clin. A Gd. Chall., pp. 109-116, 2007
  22. R. Susomboon, "A hybrid approach for liver segmentation," ... Segmentation Clin. ..., vol. i, pp. 151-160, 2007
  23. L. Rusko and G. Bekes, "Fully automatic liver segmentation for contrast-enhanced CT images," Int. Conf. Med. Image Comput. Co mput. Interv. Segmentation Clin. a Gd. challenge, 2007, pp. 143-150, 2007
  24. S. J. Lim, Y. Y. Jeong, and Y. S. Ho, "Automatic liver segmentation for volume measurement in CT Images," J. Vis. Commun. Image Represent., vol. 17, no. 4, pp. 860-875, 2006 https://doi.org/10.1016/j.jvcir.2005.07.001
  25. A. B. B, I. Diamant, E. Klang, and M. Amitai, "Fully Convolutional Network for Liver," Deep Learn. Data Labeling Med. Appl., vol. 1, pp. 77-85, 2016
  26. H. T. Tran, A. R. Oh, I. S. Na, and S. H. Kim, "Liver Segmentation and 3D Modeling from Abdominal CT Images," Smart Media J., vol. 5, no. 1, pp. 49-54, 2016
  27. P. Bilic et al., "The Liver Tumor Segmentation Benchmark (LiTS)," pp. 1-43, 2019
  28. X. Li, H. Chen, X. Qi, Q. Dou, C. W. Fu, and P. A. Heng, "H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes," IEEE Trans. Med. Imaging, vol. 37, no. 12, pp. 2663-2674, 2018 https://doi.org/10.1109/TMI.2018.2845918
  29. S. Chen, K. Ma, and Y. Zheng, "Med3D: Transfer Learning for 3D Medical Image Analysis," pp. 1-12, 2019
  30. Y. Yuan, "Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation," vol. i, pp. 3-6, 2017
  31. A. Buslaev, A. Parinov, E. Khvedchenya, V. I. Iglovikov, and A. A. Kalinin, "Albumentations: fast and flexible image augmentations," 2018
  32. C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. Jorge Cardoso, "Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10553 LNCS, pp. 240-248, 2017