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Deep dynamic spiking neural P systems with applications in organ segmentation

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Abstract

This paper proposes a new kind of spiking neural P systems, named as DeDSN P systems (deep dynamic spiking neural P systems), which combines spiking neural P system (SN P system, in short) with convolution neural network (CNN, in short), used for organ segmentation. To verifying the efficiency of DeDSN P system, it is used for segmenting pancreas, which is a significant work for many clinical applications including diagnostic assessments of diabetes or pancreatic cancer. However, the task is difficult since pancreas has small size, variable location and diverse shapes. Therefore, DeDSN P systems with two sub-systems is provided to solve the problem. In the first sub-system, we concentrate on locating the region of pancreas. Based on the obtained region, we run the second sub-system to get the accurate segmentation of pancreas. Evaluations on the public NIH pancreas dataset show an average Dice Similarity Coefficient (DSC) of 81.94% and standard deviation is 10.39%. Results show that the proposed DeDSN P systems has a good performance in comparison with some of the state-of-the-art algorithms. That is also meaningful to the development of membrane computing.

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References

  1. Attiyeh, M. A., Chakraborty, J., Doussot, A., Langdon-Embry, L., Mainarich, S., Gönen, M., Balachandran, V. P., D’Angelica, M. I., DeMatteo, R. P., Jarnagin, W. R., Kingham, T. P., Allen, P., Simpson, A., & Do MD, R. (2018). Survival prediction in pancreatic ductal adenocarcinoma by quantitative computed tomography image analysis. Annals of Surgical Oncology 25(4), 1034–1042

  2. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 3431–3440

  3. Roth, H. R., Lu, L., Lay, N., Harrison, A. P., Farag, A., Sohn, A., & Summers, R. M. (2018). Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Medical Image Analysis, 45, 94–107. https://doi.org/10.1016/j.media.2018.01.006.

    Article  Google Scholar 

  4. Farag, A., Lu, L., Roth, H. R., Liu, J., Turkbey, E., & Summers, R. M. (2017). A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. IEEE Transactions on Image Processing, 26(1), 386–399. https://doi.org/10.1109/TIP.2016.2624198

    Article  MATH  Google Scholar 

  5. Cai, J., Lu, L., Xie, Y., Xing, F., & Yang, L. (2017). Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function. CoRR 72(2), 173–196. arXiv:1707.04912

  6. Zhou, Y., Xie, L., Shen, W., Wang, Y., Fishman, E. K., & Yuille, A. L. (2017). A fixed-point model for pancreas segmentation in abdominal ct scans. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (Eds.) International conference on medical image computing and computer-assisted intervention, pp. 693–701. Springer. https://doi.org/10.1007/978-3-319-66182-7_79

  7. Zhu, Z., Xia, Y., Shen, W., Fishman, E. K., & Yuille, A. L. (2017). A 3d coarse-to-fine framework for automatic pancreas segmentation. arXiv:1712.00201

  8. Roth, H., Oda, M., Shimizu, N., Oda, H., Hayashi, Y., Kitasaka, T., Fujiwara, M., Misawa, K., & Mori, K. (2018). Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks. In: Angelini, E.D., Landman, B.A. (Eds.) Medical imaging 2018: image processing. International Society for Optics and Photonics, vol. 10574, pp. 59–64. https://doi.org/10.1117/12.2293499

  9. Ionescu, M., Păun, G., & Yokomori, T. (2006). Spiking neural p systems. Fundamenta informaticae, 71(2–3), 279–308

  10. Rong, H., Duan, Y., & Zhang, G. (2022). A bibliometric analysis of membrane computing (1998–2019). Journal of Membrane Computing, 4(2), 177–207.

    Article  Google Scholar 

  11. Zhang, G., Pérez-Jiménez, M. J., & Gheorghe, M. (2017). Real-life applications with membrane computing, vol. 25. Springer International Publishing. https://doi.org/10.1007/978-3-319-55989-6

  12. Song, T., Pan, L., Wang, J., Venkat, I., Subramanian, K., & Abdullah, R. (2012). Normal forms of spiking neural p systems with anti-spikes. IEEE Transactions on Nanobioscience, 11(4), 352–359. https://doi.org/10.1109/TNB.2012.2208122

    Article  Google Scholar 

  13. Liu, C., & Fan, L. (2016). A hybrid evolutionary algorithm based on tissue membrane systems and cma-es for solving numerical optimization problems. Knowledge-Based Systems, 105, 38–47. https://doi.org/10.1016/j.knosys.2016.04.025

    Article  Google Scholar 

  14. Song, T., Zou, Q., Liu, X., & Zeng, X. (2015). Asynchronous spiking neural p systems with rules on synapses. Neurocomputing, 151, 1439–1445. https://doi.org/10.1016/j.neucom.2014.10.044

    Article  Google Scholar 

  15. Wang, J., Shi, P., Peng, H., Pérez-Jiménez, M. J., & Wang, T. (2012). Weighted fuzzy spiking neural p systems. IEEE Transactions on Fuzzy Systems, 21(2), 209–220. https://doi.org/10.1109/TFUZZ.2012.2208974

    Article  Google Scholar 

  16. Zhang, G., Rong, H., Paul, P., He, Y., Neri, F., & Pérez-Jiménez, M. J. (2021). A complete arithmetic calculator constructed from spiking neural p systems and its application to information fusion. International Journal of Neural Systems, 31(1), 2050055. https://doi.org/10.1142/S0129065720500550

    Article  Google Scholar 

  17. Peng, H., Wang, J., Pérez-Jiménez, M. J., Wang, H., Shao, J., & Wang, T. (2013). Fuzzy reasoning spiking neural p system for fault diagnosis. Information Sciences, 235, 106–116. https://doi.org/10.1016/j.ins.2012.07.015

    Article  MATH  Google Scholar 

  18. Díaz-Pernil, D., Berciano, A., Peña-Cantillana, F., & Gutiérrez-Naranjo, M. A. (2013). Segmenting images with gradient-based edge detection using membrane computing. Pattern Recognition Letters, 34(8), 846–855. https://doi.org/10.1016/j.patrec.2012.10.014

    Article  Google Scholar 

  19. Zhang, G., Zhang, X., Rong, H., Paul, P., Zhu, M., Neri, F., & Ong, Y.-S. (2022). A layered spiking neural system for classification problems. International Journal of Neural Systems, 32(8), 2250023. https://doi.org/10.1142/S012906572250023X

    Article  Google Scholar 

  20. Peng, H., Shi, P., Wang, J., Riscos-Núñez, A., & Pérez-Jiménez, M. J. (2017). Multiobjective fuzzy clustering approach based on tissue-like membrane systems. Knowledge-Based Systems, 125, 74–82. https://doi.org/10.1016/j.knosys.2017.03.024

    Article  Google Scholar 

  21. Zhu, M., Yang, Q., Dong, J., Zhang, G., Gou, X., Rong, H., Paul, P., & Neri, F. (2021). An adaptive optimization spiking neural p system for binary problems. International Journal of Neural Systems, 31(1), 2050054.

    Article  Google Scholar 

  22. Dong, J., Zhang, G., Luo, B., Yang, Q., Guo, D., Rong, H., Zhu, M., & Zhou, K. (2022). A distributed adaptive optimization spiking neural p system for approximately solving combinatorial optimization problems. Information Sciences, 596, 1–14. https://doi.org/10.1016/j.ins.2022.03.007

    Article  Google Scholar 

  23. Liu, X., & Xue, J. (2017). A cluster splitting technique by hopfield networks and p systems on simplices. Neural Processing Letters, 46(1), 171–194. https://doi.org/10.1007/s11063-016-9577-z

    Article  Google Scholar 

  24. Zhang, G., Rong, H., Neri, F., & Pérez-Jiménez, M. J. (2014). An optimization spiking neural p system for approximately solving combinatorial optimization problems. International Journal of Neural Systems, 24(05), 1440006. https://doi.org/10.1142/S0129065714400061 PMID: 24875789.

    Article  Google Scholar 

  25. Zhang, G., Cheng, J., Gheorghe, M., & Meng, Q. (2013). A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Applied Soft Computing, 13(3), 1528–1542. https://doi.org/10.1016/j.asoc.2012.05.032

    Article  Google Scholar 

  26. Cabarle, F. G. C., Adorna, H., & Martínez, M. A. (2012). A spiking neural p system simulator based on cuda. In: Gheorghe, M., Păun, G., Rozenberg, G., Salomaa, A., Verlan, S. (Eds.) Membrane computing, vol. 7184, pp. 87–103. Springer. https://doi.org/10.1007/978-3-642-28024-5_8

  27. Organick, L., Ang, S. D., Chen, Y.-J., Lopez, R., Yekhanin, S., Makarychev, K., et al. (2018). Random access in large-scale dna data storage. Nature Biotechnology, 36(3), 242–248. https://doi.org/10.1038/nbt.4079.

    Article  Google Scholar 

  28. Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B., Summers, R. M. (2015). Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W. M., Frangi, A. (Eds.) Medical image computing and computer-assisted intervention—MICCAI 2015, pp. 556–564. Springer. https://doi.org/10.1007/978-3-319-24553-9_68

  29. 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. https://doi.org/10.1016/j.neuroimage.2014.12.061

    Article  Google Scholar 

  30. Dahl, G. E., Sainath, T. N., Hinton, G. E. (2013). Improving deep neural networks for lvcsr using rectified linear units and dropout. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp. 8609–8613

  31. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W. M., Frangi, A. F. (Eds.) Medical image computing and computer-assisted intervention—MICCAI 2015, pp. 234–241. Springer. https://doi.org/10.1007/978-3-319-24574-4_28

  32. Shamir, O., & Zhang, T. (2013). Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes. In: Dasgupta, S., McAllester, D. (Eds.) Proceedings of the 30th international conference on machine learning. Proceedings of machine learning research, vol. 28. PMLR, Atlanta, Georgia, USA, pp. 71–79. https://proceedings.mlr.press/v28/shamir13.html

  33. Heinrich, M. P., & Oktay, O. (2017). Briefnet: Deep pancreas segmentation using binary sparse convolutions. In: M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. L. Collins, & S. Duchesne (Eds.) Medical image computing and computer assisted intervention-MICCAI 2017, pp. 329–337. Springer

  34. Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019). Attention gated networks: learning to leverage salient regions in medical images. Medical Image Analysis, 53, 197–207. https://doi.org/10.1016/j.media.2019.01.012.

    Article  Google Scholar 

  35. Lee, C., Liu, J., Griffin, K., Folio, L., & Summers, R. M. (2020). Adult patient-specific CT organ dose estimations using automated segmentations and monte carlo simulations. Biomedical Physics & Engineering Express, 6(4), 045016. https://doi.org/10.1088/2057-1976/ab98e6

    Article  Google Scholar 

  36. Fejne, F., Landgren, M., Alv’en, J., Ul’en, J., Fredriksson, J., Larsson, V., Enqvist, O., & Kahl, F. (2017). Multiatlas segmentation using robust feature-based registration. In: Cloud-based benchmarking of medical image analysis, pp. 203–218. Springer

  37. Si, K., Xue, Y., Yu, X., Zhu, X., Li, Q., Gong, W., Liang, T., & Duan, S. (2021). Fully end-to-end deep-learning-based diagnosis of pancreatic tumors. Theranostics, 11(4), 1982.

    Article  Google Scholar 

  38. Knolle, M., Kaissis, G., Jungmann, F., Ziegelmayer, S., Sasse, D., Makowski, M., et al. (2021). Efficient, high-performance semantic segmentation using multi-scale feature extraction. Plos One, 16(8), 0255397.

    Article  Google Scholar 

  39. Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the thirteenth international conference on artificial intelligence and statistics. Proceedings of machine learning research, vol. 9, pp. 249–256. PMLR, Chia Laguna Resort, Sardinia, Italy. https://proceedings.mlr.press/v9/glorot10a.html

  40. Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297–302. https://doi.org/10.2307/1932409

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (nos. 62172262, 61802234, 61876101 and 61971271), the Natural Science Foundation of Shandong Province (no. ZR2019QF007).

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Authors and Affiliations

  1. Business School, Academy of Management Science, Shandong Normal University, Jinan, 250014, Shandong, China

    Chenggong Qiu, Xiyu Liu & Qi Li

  2. Business School, Academy of Management Science, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, Shandong Normal University, Jinan, 250014, Shandong, China

    Jie Xue

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Qiu, C., Xue, J., Liu, X. et al. Deep dynamic spiking neural P systems with applications in organ segmentation. J Membr Comput 4, 329–340 (2022). https://doi.org/10.1007/s41965-022-00115-4

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