Abstract
Pneumothorax is a critical abnormality that shall be treated with higher priority, and hence a computerized triage scheme is needed. A deep-learning-based framework to automatically segment the pneumothorax in chest X-rays is developed to support the realization of a triage system. Since a large number of pixel-level annotations is commonly needed but difficult to obtain for deep learning model, we propose a weakly supervised framework that allows partial training data to be weakly annotated with only image-level labels. We employ the attention masks derived from an image-level classification model as the pixel-level masks for those weakly-annotated data. Because the attention masks are rough and may have errors, we further develop a spatial label smoothing regularization technique to explore the uncertainty for the incorrectness of the attention masks in the training of segmentation model. Experimental results show that the proposed weakly supervised segmentation algorithm relieves the need of well-annotated data and yield satisfactory performance on the pneumothorax segmentation.
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
References
Annarumma, M., Withey, S.J., Bakewell, R.J., Pesce, E., Goh, V., Montana, G.: Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology 291, 180921 (2019)
Cai, J., Lu, L., Harrison, A.P., Shi, X., Chen, P., Yang, L.: Iterative attention mining for weakly supervised thoracic disease pattern localization in chest X-Rays. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 589–598. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_66
Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1–4. IEEE (2017)
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. IEEE (2017)
Kelly, B.S., Rainford, L.A., Darcy, S.P., Kavanagh, E.C., Toomey, R.J.: The development of expertise in radiology: in chest radiograph interpretation,"expert" search pattern may predate "expert" levels of diagnostic accuracy for pneumothorax identification. Radiology 280(1), 252–260 (2016)
Li, K., Wu, Z., Peng, K.C., Ernst, J., Fu, Y.: Tell me where to look: guided attention inference network. arXiv preprint arXiv:1802.10171 (2018)
Li, Z., et al.: Thoracic disease identification and localization with limited supervision. arXiv preprint arXiv:1711.06373 (2017)
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
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)
Yan, C., Yao, J., Li, R., Xu, Z., Huang, J.: Weakly supervised deep learning for thoracic disease classification and localization on chest x-rays. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 103–110. ACM (2018)
Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 472–480 (2017)
Zarogoulidis, P., et al.: Pneumothorax: from definition to diagnosis and treatment. J. Thorac. Dis. 6(Suppl 4), S372 (2014)
Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3754–3762 (2017)
Acknowledgement
This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400), STCSM grants (19QC1400600, 17411953300), and the Shanghai Municipal Commission of Economy and Informatization (2017RGZN01026).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ouyang, X. et al. (2019). Weakly Supervised Segmentation Framework with Uncertainty: A Study on Pneumothorax Segmentation in Chest X-ray. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_68
Download citation
DOI: https://doi.org/10.1007/978-3-030-32226-7_68
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32225-0
Online ISBN: 978-3-030-32226-7
eBook Packages: Computer ScienceComputer Science (R0)
