Advertisement

Characterizing robustness and sensitivity of convolutional neural networks for quantitative analysis of mitochondrial morphology

  • Xiaoqi Chai
  • Qinle Ba
  • Ge Yang
Research Article
  • 7 Downloads

Abstract

Background

Quantitative analysis of mitochondrial morphology plays important roles in studies of mitochondrial biology. The analysis depends critically on segmentation of mitochondria, the image analysis process of extracting mitochondrial morphology from images. The main goal of this study is to characterize the performance of convolutional neural networks (CNNs) in segmentation of mitochondria from fluorescence microscopy images. Recently, CNNs have achieved remarkable success in challenging image segmentation tasks in several disciplines. So far, however, our knowledge of their performance in segmenting biological images remains limited. In particular, we know little about their robustness, which defines their capability of segmenting biological images of different conditions, and their sensitivity, which defines their capability of detecting subtle morphological changes of biological objects.

Methods

We have developed a method that uses realistic synthetic images of different conditions to characterize the robustness and sensitivity of CNNs in segmentation of mitochondria. Using this method, we compared performance of two widely adopted CNNs: the fully convolutional network (FCN) and the U-Net. We further compared the two networks against the adaptive active-mask (AAM) algorithm, a representative of high-performance conventional segmentation algorithms.

Results

The FCN and the U-Net consistently outperformed the AAM in accuracy, robustness, and sensitivity, often by a significant margin. The U-Net provided overall the best performance.

Conclusions

Our study demonstrates superior performance of the U-Net and the FCN in segmentation of mitochondria. It also provides quantitative measurements of the robustness and sensitivity of these networks that are essential to their applications in quantitative analysis of mitochondrial morphology.

Keywords

convolutional neural network mitochondrial morphology image segmentation robustness sensitivity 

Notes

Acknowledgements

Xiaoqi Chai acknowledges support of a Ji-Dian Liang Graduate Research Fellowship. Qinle Ba acknowledges support of a Bertucci Graduate Research Fellowship. Ge Yang acknowledges support of NSF CAREER grant DBI-1149494 and NSF grant CBET-1804929. The authors would also like to thank Yile Feng and Weicheng Lin for their technical assistance.

Supplementary material

40484_2018_156_MOESM1_ESM.pdf (1011 kb)
Supplementary material, approximately 1012 KB.

References

  1. 1.
    McBride, H. M., Neuspiel, M. and Wasiak, S. (2006) Mitochondria: more than just a powerhouse. Curr. Biol., 16, R551–R560CrossRefGoogle Scholar
  2. 2.
    Nunnari, J. and Suomalainen, A. (2012) Mitochondria: in sickness and in health. Cell, 148, 1145–1159CrossRefGoogle Scholar
  3. 3.
    Karbowski, M. and Youle, R. J. (2003) Dynamics of mitochondrial morphology in healthy cells and during apoptosis. Cell Death Differ., 10, 870–880CrossRefGoogle Scholar
  4. 4.
    Campello, S. and Scorrano, L. (2010) Mitochondrial shape changes: orchestrating cell pathophysiology. EMBO Rep., 11, 678–684CrossRefGoogle Scholar
  5. 5.
    Chen, K. C. J., Yu, Y. Y., Li, R. Q., Lee, H. C., Yang, G. and Kovacevic, J. (2012) Adaptive active-mask image segmentation for quantitative characterization of mitochondrial morphology. In Proceedings of 2012 IEEE International Conference on Image Processing (ICIP), pp. 2033–2036CrossRefGoogle Scholar
  6. 6.
    Leonard, A. P., Cameron, R. B., Speiser, J. L., Wolf, B. J., Peterson, Y. K., Schnellmann, R. G., Beeson, C. C. and Rohrer, B. (2015) Quantitative analysis of mitochondrial morphology and membrane potential in living cells using high-content imaging, machine learning, and morphological binning. Biochim. Biophys. Acta, 1853, 348–360CrossRefGoogle Scholar
  7. 7.
    Peng, J.-Y., Lin, C.-C., Chen, Y.-J., Kao, L.-S., Liu, Y.-C., Chou, C.-C., Huang, Y.-H., Chang, F.-R., Wu, Y.-C., Tsai, Y.-S., et al. (2011) Automatic morphological subtyping reveals new roles of caspases in mitochondrial dynamics. PLoS Comput. Biol., 7, e1002212CrossRefGoogle Scholar
  8. 8.
    Iannetti, E. F., Smeitink, J. A. M., Beyrath, J., Willems, P. H. G. M. and Koopman, W. J. H. (2016) Multiplexed high-content analysis of mitochondrial morphofunction using live-cell microscopy. Nat. Protoc., 11, 1693–1710CrossRefGoogle Scholar
  9. 9.
    Daniele, J. R., Esping, D. J., Garcia, G., Parsons, L. S., Arriaga, E. A. and Dillin, A. (2017) High-throughput characterization of region-specific mitochondrial function and morphology. Sci. Rep., 7, 6749CrossRefGoogle Scholar
  10. 10.
    Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012) ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems. pp. 1097–1105Google Scholar
  11. 11.
    LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep learning. Nature, 521, 436–444CrossRefGoogle Scholar
  12. 12.
    Sadanandan, S. K., Ranefall, P., Le Guyader, S. and Wählby, C. (2017) Automated training of deep convolutional neural networks for cell segmentation. Sci. Rep., 7, 7860CrossRefGoogle Scholar
  13. 13.
    Kraus, O. Z., Ba, J. L. and Frey, B. J. (2016) Classifying and segmenting microscopy images with deep multiple instance learning. Bioinformatics, 32, i52–i59CrossRefGoogle Scholar
  14. 14.
    Xing, F., Xie, Y., Su, H., Liu, F. and Yang, L. (2017) Deep learning in microscopy image analysis: a survey. In IEEE Transactions on Neural Networks and Learning Systems. pp. 1–19Google Scholar
  15. 15.
    Yu, Y., Lee, H.-C., Chen, K.-C., Suhan, J., Qiu, M., Ba, Q. and Yang, G. (2016) Inner membrane fusion mediates spatial distribution of axonal mitochondria. Sci. Rep., 6, 18981CrossRefGoogle Scholar
  16. 16.
    Long, J., Shelhamer, E. and Darrell, T. (2015) Fully convolutional networks for semantic segmentation. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3431–3440CrossRefGoogle Scholar
  17. 17.
    Ronneberger, O., Fischer, P. and Brox, T. (2015) U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention. 9351, 234–241Google Scholar
  18. 18.
    Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H. and Yli-Harja, O. (2007) Computational framework for simulating fluorescence microscope images with cell populations. IEEE Trans. Med. Imaging, 26, 1010–1016CrossRefGoogle Scholar
  19. 19.
    Li, C., Huang, R., Ding, Z., Gatenby, J. C., Metaxas, D. N. and Gore, J. C. (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process., 20, 2007–2016CrossRefGoogle Scholar
  20. 20.
    Ngo, T. A., Lu, Z. and Carneiro, G. (2017) Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med. Image Anal., 35, 159–171CrossRefGoogle Scholar
  21. 21.
    Schneider, C. A., Rasband, W. S. and Eliceiri, K. W. (2012) NIH Image to ImageJ: 25 years of image analysis. Nat. Methods, 9, 671–675CrossRefGoogle Scholar
  22. 22.
    Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) TensorFlow: a system for large-scale machine learning. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation. pp. 265–283Google Scholar
  23. 23.
    Heimann, T., van Ginneken, B., Styner, M. A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al. (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imaging, 28, 1251–1265CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Computational BiologyCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations