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Deep Convolutional Neural Networks in Application to Kidney Segmentation in the DCE-MR Images

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12744))

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Abstract

This paper evaluates three convolutional neural network architectures – U-Net, SegNet, and Fully Convolutional (FC) DenseNets – in application to kidney segmentation in the dynamic contrast-enhanced magnetic resonance images (DCE-MRI). We found U-Net to outperform the alternative solutions with the Jaccard coefficient equal to 94% against 93% and 91% for SegNet and FCDenseNets, respectively. As a next step, we propose to classify renal mask voxels into cortex, medulla, and pelvis based on temporal characteristics of signal intensity time courses. We evaluate our computational framework on a set of 20 DCE-MRI series by calculating image-derived glomerular filtration rates (GFR) – an indicator of renal tissue state. Then we compare our calculated GFR with the available ground-truth values measured in the iohexol clearance tests. The mean bias between the two measurements amounts to −7.4 ml/min/1.73 m2 which proves the reliability of the designed segmentation pipeline.

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References

  1. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017). https://doi.org/10.1109/TPAMI.2016.2644615

    Article  Google Scholar 

  2. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49

    Chapter  Google Scholar 

  3. Choi, Y., et al.: The initial area under the curve derived from dynamic contrast-enhanced MRI improves prognosis prediction in glioblastoma with unmethylated mgmt promoter. Am. J. Neuroradiol. 38(8), 1528–1535 (2017)

    Article  Google Scholar 

  4. Cutajar, M., Mendichovszky, I., Tofts, P., Gordon, I.: The importance of AIF ROI selection in DCE-MRI renography: reproducibility and variability of renal perfusion and filtration. Eur. J. Radiol. 74(3), e154–e60 (2010)

    Article  Google Scholar 

  5. Haghighi, M., Warfield, S.K., Kurugol, S.: Automatic renal segmentation in DCE-MRI using convolutional neural networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1534–1537 (2018). https://doi.org/10.1109/ISBI.2018.8363865

  6. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1–11, December 2015

    Google Scholar 

  7. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017). https://doi.org/10.1109/CVPR.2017.243

  8. Johnson, H.J., McCormick, M.M., Ibanez, L.: The ITK Software Guide: Design and Functionality. Kitware (2020)

    Google Scholar 

  9. Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1175–1183 (2017). https://doi.org/10.1109/CVPRW.2017.156

  10. Khan, Z., Yahya, N., Alsaih, K., Ali, S.S.A., Meriaudeau, F.: Evaluation of deep neural networks for semantic segmentation of prostate in T2W MRI. Sensors 20(11), 3183 (2020). https://doi.org/10.3390/s20113183

    Article  Google Scholar 

  11. Kline, T.L., et al.: Performance of an artificial multi-observer deep neural network for fully automated segmentation of polycystic kidneys. J. Digit. Imaging 30(4), 442–448 (2017). https://doi.org/10.1007/s10278-017-9978-1

    Article  Google Scholar 

  12. Lee, V.S., et al.: Renal function measurements from MR renography and a simplified multicompartmental model. Am. J. Physiol.-Renal Physiol. 292(5), F1548–F1559 (2007). https://doi.org/10.1152/ajprenal.00347.2006

    Article  Google Scholar 

  13. Li, S., et al.: Wavelet-based segmentation of renal compartments in dce-mri of human kidney: Initial results in patients and healthy volunteers. Comput. Med. Imaging Graph. 36, 108–18 (2012). https://doi.org/10.1016/j.compmedimag.2011.06.005

    Article  Google Scholar 

  14. Liu, S., Deng, W.: Very deep convolutional neural network based image classification using small training sample size. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 730–734 (2015). https://doi.org/10.1109/ACPR.2015.7486599

  15. 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). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  16. Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017). https://doi.org/10.1109/TPAMI.2016.2572683

    Article  Google Scholar 

  17. Sourbron, S.P., Michaely, H.J., Reiser, M.F., Schoenberg, S.O.: MRI-measurement of perfusion and glomerular filtration in the human kidney with a separable compartment model. Investigative Radiology 43(1), 40–48 (2008). https://doi.org/10.1097/rli.0b013e31815597c5

  18. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  19. Tofts, P., Cutajar, M., Mendichovszky, I., Peters, A., Gordon, I.: Precise measurement of renal filtration and vascular parameters using a two-compartment model for dynamic contrast-enhanced MRI of the kidney gives realistic normal values. Eur. Radiol. 22, 1320–30 (2012). https://doi.org/10.1007/s00330-012-2382-9

    Article  Google Scholar 

  20. Ulas, C., et al.: Convolutional neural networks for direct inference of pharmacokinetic parameters: Application to stroke dynamic contrast-enhanced MRI. Frontiers Neurol. 9, 1147 (2019). https://doi.org/10.3389/fneur.2018.01147

  21. Yang, X., Le Minh, H., (Tim) Cheng, K.T., Sung, K.H., Liu, W.: Renal compartment segmentation in dce-mri images. Med. Image Anal. 32(C), 269–280 (2016). https://doi.org/10.1016/j.media.2016.05.006

  22. Yoruk, U., Hargreaves, B.A., Vasanawala, S.S.: Automatic renal segmentation for MR urography using 3D-GrabCut and random forests. Magn. Reson. Med. 79(3), 1696–1707 (2018). https://doi.org/10.1002/mrm.26806

    Article  Google Scholar 

  23. Zöllner, F., et al.: Assessment of 3d dce-mri of the kidneys using non-rigid image registration and segmentation of voxel time courses. Comput. Med. Imaging Graph. 33, 171–81 (2009). https://doi.org/10.1016/j.compmedimag.2008.11.004

    Article  Google Scholar 

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Authors and Affiliations

  1. Lodz University of Technology, Łódź, Poland

    Artur Klepaczko

  2. Department Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway

    Eli Eikefjord & Arvid Lundervold

  3. Department of Biomedicine, University of Bergen, Bergen, Norway

    Arvid Lundervold

  4. Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway

    Arvid Lundervold

Authors
  1. Artur Klepaczko
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  2. Eli Eikefjord
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  3. Arvid Lundervold
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Corresponding author

Correspondence to Artur Klepaczko .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Klepaczko, A., Eikefjord, E., Lundervold, A. (2021). Deep Convolutional Neural Networks in Application to Kidney Segmentation in the DCE-MR Images. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_50

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  • DOI: https://doi.org/10.1007/978-3-030-77967-2_50

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  • Online ISBN: 978-3-030-77967-2

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