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A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging

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Medical Image Learning with Limited and Noisy Data (MILLanD 2023)

Abstract

Supervised deep learning methods offer the potential for automating lesion segmentation in routine clinical brain imaging, but performance is dependent on label quality. In practice, obtaining high-quality labels from experienced annotators for large-scale datasets is not always feasible, while noisy labels from less experienced annotators are often available. Prior studies focus on either label refinement methods or on learning to segment with noisy labels, but there has been little work on integrating these approaches within a unified framework. To address this gap, we propose a novel multitask framework for end-to-end noisy-label refinement and lesion segmentation. Our approach minimizes the discrepancy between the refined label and the predicted segmentation mask, and is highly customizable for scenarios with multiple sets of noisy labels, incomplete ground truth coverage and/or 2D/3D scans. In extensive experiments on both proprietary and public clinical brain imaging datasets, we demonstrate that our end-to-end framework offers strong performance improvements over prevailing baselines on both label refinement and lesion segmentation. Our proposed framework maintains performance gains over baselines even when ground truth labels are available for only 25–50% of the dataset. Our approach has implications for effective medical image segmentation in settings that are replete with noisy labels but sparse on ground truth annotation.

Y. Yu and J. Wang—Contributed equally to this work.

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References

  1. Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digital Imaging 30, 449–459 (2017)

    Article  Google Scholar 

  2. Bertels, J., et al.: Optimizing the dice score and jaccard index for medical image segmentation: theory and practice. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 92–100. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_11

    Chapter  Google Scholar 

  3. Cai, Z., Xin, J., Shi, P., Zhou, S., Wu, J., Zheng, N.: Meta pixel loss correction for medical image segmentation with noisy labels. In: Medical Image Learning with Limited and Noisy Data: First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. pp. 32–41. Springer (2022). https://doi.org/10.1007/978-3-031-16760-7_4

  4. Cheng, G., Ji, H., Tian, Y.: Walking on two legs: learning image segmentation with noisy labels. In: Conference on Uncertainty in Artificial Intelligence, pp. 330–339. PMLR (2020)

    Google Scholar 

  5. Guo, R., Pagnucco, M., Song, Y.: Learning with noise: mask-guided attention model for weakly supervised nuclei segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 461–470. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_43

    Chapter  Google Scholar 

  6. He, K., Zhang, X., Ren, S., Sun, J.: Identity Mappings in Deep Residual Networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38

    Chapter  Google Scholar 

  7. Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993)

    Article  Google Scholar 

  8. Karimi, D., Dou, H., Warfield, S.K., Gholipour, A.: Deep learning with noisy labels: exploring techniques and remedies in medical image analysis. Med. Image Anal. 65, 101759 (2020)

    Article  Google Scholar 

  9. Kim, J., Kim, M., Kang, H., Lee, K.: U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. arXiv preprint arXiv:1907.10830 (2019)

  10. Lee, D.H., et al.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML. p. 896 (2013)

    Google Scholar 

  11. Lee, J., et al.: A pixel-level coarse-to-fine image segmentation labelling algorithm. Sci. Reports 12(1), 8672 (2022)

    Google Scholar 

  12. Li, S., Gao, Z., He, X.: Superpixel-Guided Iterative Learning from Noisy Labels for Medical Image Segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 525–535. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87193-2_50

    Chapter  Google Scholar 

  13. Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)

    Article  Google Scholar 

  14. Myronenko, A.: 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28

    Chapter  Google Scholar 

  15. Obuchowicz, R., Oszust, M., Piorkowski, A.: Interobserver variability in quality assessment of magnetic resonance images. BMC Med. Imaging 20, 1–10 (2020)

    Article  Google Scholar 

  16. Rajpurkar, P., et al.: Deep learning for chest radiograph diagnosis: a retrospective comparison of the chexnext algorithm to practicing radiologists. PLoS Med. 15(11), e1002686 (2018)

    Article  Google Scholar 

  17. Ranjbarzadeh, R., Bagherian Kasgari, A., Jafarzadeh Ghoushchi, S., Anari, S., Naseri, M., Bendechache, M.: Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci. Reports 11(1), 1–17 (2021)

    Google Scholar 

  18. 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 

  19. Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23(7), 903–921 (2004)

    Article  Google Scholar 

  20. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)

    Google Scholar 

  21. Yang, Y., Wang, Z., Liu, J., Cheng, K.T., Yang, X.: Label refinement with an iterative generative adversarial network for boosting retinal vessel segmentation. arXiv preprint arXiv:1912.02589 (2019)

  22. Zhang, L., et al.: Learning to Segment When Experts Disagree. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 179–190. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_18

    Chapter  Google Scholar 

  23. Zhang, L., et al.: Disentangling human error from the ground truth in segmentation of medical images. In: Proceedings of the 34th International Conference on Neural Information Processing Systems, pp. 15750–15762. ACL (2020)

    Google Scholar 

  24. Zhang, M., et al.: Characterizing label errors: confident learning for noisy-labeled image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 721–730. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_70

    Chapter  Google Scholar 

  25. Zhen, L., Hu, P., Wang, X., Peng, D.: Deep supervised cross-modal retrieval. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10394–10403 (2019)

    Google Scholar 

  26. Zheng, G., Awadallah, A.H., Dumais, S.: Meta label correction for noisy label learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11053–11061 (2021)

    Google Scholar 

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Acknowledgements

Research efforts were supported by funding and infrastructure for deep learning and medical imaging research from the Institute for Infocomm Research, Science and Engineering Research Council, A*STAR, Singapore and the National Neuroscience Institute, Singapore.

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Correspondence to Ivan Ho Mien .

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Yu, Y. et al. (2023). A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging. In: Xue, Z., et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_6

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  • DOI: https://doi.org/10.1007/978-3-031-44917-8_6

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