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On New Convolutional Neural Network Based Algorithms for Selective Segmentation of Images

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Medical Image Understanding and Analysis (MIUA 2020)


Selective segmentation is an important aspect of image processing. Being able to reliably segment a particular object in an image has important applications particularly in medical imaging. Robust methods can aid clinicians with diagnosis, surgical planning, etc. Many selective segmentation algorithms use geometric constraints such as information from the edges in order to determine where an object lies. It is still a challenge where there is low contrast present between two objects, and an edge is difficult to detect. Relying on purely edge constraints in this case will fail. We aim to make use of area constraints in addition to edge information in a segmentation model which is robustly capable of segmenting regions in an image even in the presence of low contrast, when given suitable user input. In addition, we implement a deep learning algorithm based on this model, allowing for a supervised, semi-supervised or unsupervised approach, depending on data availability.

Work supported by UK EPSRC grant EP/N014499/1.

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  1. Badshah, N., Chen, K.: Image selective segmentation under geometrical constraints using an active contour approach. Commun. Comput. Phys. 7(4), 759 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). arXiv preprint arXiv:1901.04056 (2019)

  3. Burrows, L., Guo, W., Chen, K., Torella, F.: Edge enhancement for image segmentation using a RKHS method. In: Zheng, Y., Williams, B.M., Chen, K. (eds.) MIUA 2019. CCIS, vol. 1065, pp. 198–207. Springer, Cham (2020).

    Chapter  Google Scholar 

  4. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  5. Chan, T.F., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. Appl. Math. 66(5), 1632–1648 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  7. Chen, X., Williams, B.M., Vallabhaneni, S.R., Czanner, G., Williams, R., Zheng, Y.: Learning active contour models for medical image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11632–11640 (2019)

    Google Scholar 

  8. Gout, C., Le Guyader, C., Vese, L.: Segmentation under geometrical conditions using geodesic active contours and interpolation using level set methods. Numer. Algorithms 39(1–3), 155–173 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. Jumaat, A.K., Chen, K.: A reformulated convex and selective variational image segmentation model and its fast multilevel algorithm. Numer. Math. Theory Methods Appl. 12(2), 403–437 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  10. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  MATH  Google Scholar 

  11. Lu, T., Neittaanmaki, P., Tai, X.-C.: A parallel splitting-up method for partial differential equations and its applications to Navier-stokes equations. ESAIM Math. Model. Numer. Anal. 26(6), 673–708 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  12. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5), 577–685 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  13. Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  14. Rada, L., Chen, K.: Improved selective segmentation model using one level-set. J. Algorithms Comput. Technol. 7(4), 509–540 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  15. Roberts, M., Chen, K., Irion, K.L.: A convex geodesic selective model for image segmentation. J. Math. Imaging Vis. 61(4), 482–503 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  16. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).

    Chapter  Google Scholar 

  17. Sethian, J.A.: Fast marching methods. SIAM Rev. 41(2), 199–235 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  18. Spencer, J., Chen, K.: A convex and selective variational model for image segmentation. Commun. Math. Sci. 13(6), 1453–1472 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  19. Weickert, J., Romeny, B.T.H., Viergever, M.A.: Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans. Image Process. 7(3), 398–410 (1998)

    Article  Google Scholar 

  20. Zhao, H.: A fast sweeping method for Eikonal equations. Math. Comput. 74(250), 603–627 (2005)

    Article  MathSciNet  MATH  Google Scholar 

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Burrows, L., Chen, K., Torella, F. (2020). On New Convolutional Neural Network Based Algorithms for Selective Segmentation of Images. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham.

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  • Print ISBN: 978-3-030-52790-7

  • Online ISBN: 978-3-030-52791-4

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