Abstract
Mitochondria segmentation from electron microscopy images has seen great progress, especially for learning-based methods. However, since the learning of model requires massive annotations, it is time and labour expensive to learn a specific model for each acquired dataset. On the other hand, it is challenging to generalize a learned model to datasets of unknown species or those acquired by unknown devices, mainly due to the difference of data distributions. In this paper, we study unsupervised domain adaptation to enhance the generalization capacity, where no annotation for target datasets is required. We start from an effective solution, which learns the target data distribution with pseudo labels predicted by a source-domain model. However, the obtained pseudo labels are usually noisy due to the domain gap. To address this issue, we propose an uncertainty-aware model to rectify noisy labels. Specifically, we insert Monte-Carlo dropout layers to a UNet backbone, where the uncertainty is measured by the standard deviation of predictions. Experiments on MitoEM and FAFB datasets demonstrate the superior performance of proposed model, in terms of the adaptations between different species and acquisition devices.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bermúdez-Chacón, R., Altingövde, O., Becker, C., Salzmann, M., Fua, P.: Visual correspondences for unsupervised domain adaptation on electron microscopy images. IEEE Trans. Med. Imaging 39(4), 1256–1267 (2019)
Bermúdez-Chacón, R., Becker, C., Salzmann, M., Fua, P.: Scalable unsupervised domain adaptation for electron microscopy. In: MICCAI (2016)
Bermúdez-Chacón, R., Márquez-Neila, P., Salzmann, M., Fua, P.: A domain-adaptive two-stream U-net for electron microscopy image segmentation. In: ISBI (2018)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Choi, J., Jeong, M., Kim, T., Kim, C.: Pseudo-labeling curriculum for unsupervised domain adaptation. In: BMVC (2019)
Funke, J.: Automatic neuron reconstruction from anisotropic electron microscopy volumes. Ph.D. thesis, ETH Zurich (2014)
Gal, Y., Ghahramani, Z.: Bayesian convolutional neural networks with Bernoulli approximate variational inference. In: ICLR (2016)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: ICML (2016)
Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 521(7553), 452–459 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: ICCV (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
Jungo, A., Reyes, M.: Assessing reliability and challenges of uncertainty estimations for medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 48–56. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_6
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Lee, K., Zung, J., Li, P., Jain, V., Seung, H.S.: Superhuman accuracy on the SNEMI3D connectomics challenge. arXiv preprint arXiv:1706.00120 (2017)
Lee, S., Kim, D., Kim, N., Jeong, S.G.: Drop to adapt: learning discriminative features for unsupervised domain adaptation. In: ICCV (2019)
Li, G., Kang, G., Liu, W., Wei, Y., Yang, Y.: Content-consistent matching for domain adaptive semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 440–456. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58568-6_26
Lin, R., Zeng, X., Kitani, K., Xu, M.: Adversarial domain adaptation for cross data source macromolecule in situ structural classification in cellular electron cryo-tomograms. Bioinformatics 35(14), i260–i268 (2019)
Liu, D., et al.: Unsupervised instance segmentation in microscopy images via panoptic domain adaptation and task re-weighting. In: CVPR (2020)
Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Struct. 405(2), 442–451 (1975)
Roels, J., Hennies, J., Saeys, Y., Philips, W., Kreshuk, A.: Domain adaptive segmentation in volume electron microscopy imaging. In: ISBI (2019)
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
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(1), 1929–1958 (2014)
Wei, D., et al.: MitoEM dataset: large-scale 3D mitochondria instance segmentation from EM images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 66–76. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59722-1_7
Xing, F., Bennett, T., Ghosh, D.: Adversarial domain adaptation and pseudo-labeling for cross-modality microscopy image quantification. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 740–749. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_82
Zheng, Z., Yang, Y.: Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation. Int. J. Comput. Vision 129(4), 1106–1120 (2021)
Zheng, Z., et al.: A complete electron microscopy volume of the brain of adult drosophila melanogaster. Cell 174(3), 730–743 (2018)
Acknowledgements
This work was supported by the University Synergy Innovation Program of Anhui Province No. GXXT-2019-025 and the National Natural Science Foundation of China (NSFC) under Grant 62076230.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, S., Chen, C., Xiong, Z., Chen, X., Sun, X. (2021). Uncertainty-Aware Label Rectification for Domain Adaptive Mitochondria Segmentation. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12903. Springer, Cham. https://doi.org/10.1007/978-3-030-87199-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-87199-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87198-7
Online ISBN: 978-3-030-87199-4
eBook Packages: Computer ScienceComputer Science (R0)
