Abstract
Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, however, expert manual segmentation often relies on the boundaries of anatomical structures of interest. We propose boundary aware CNNs for medical image segmentation. Our networks are designed to account for organ boundary information, both by providing a special network edge branch and edge-aware loss terms, and they are trainable end-to-end. We validate their effectiveness on the task of brain tumor segmentation using the BraTS 2018 dataset. Our experiments reveal that our approach yields more accurate segmentation results, which makes it promising for more extensive application to medical image segmentation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Acuna, D., Kar, A., Fidler, S.: Devil is in the edges: Learning semantic boundaries from noisy annotations. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: International Conference on Learning Representations (ICLR) (2019)
Hatamizadeh, A., et al.: Deep active lesion segmentation. arXiv preprint arXiv:1908.06933 (2019)
Hatamizadeh, A., Hosseini, H., Liu, Z., Schwartz, S.D., Terzopoulos, D.: Deep dilated convolutional nets for the automatic segmentation of retinal vessels. arXiv preprint arXiv:1905.12120 (2019)
Hu, Y., Zou, Y., Feng, J.: Panoptic edge detection. https://arxiv.org/abs/1906.00590 (2019)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth International Conference on 3D Vision (3DV) (2016)
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
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
Takikawa, T., Acuna, D., Jampani, V., Fidler, S.: Gated-SCNN: gated shape CNNs for semantic segmentation. arXiv preprint arXiv:1907.05740 (2019)
Yu, Z., Feng, C., Liu, M., Ramalingam, S.: CASENet: deep category-aware semantic edge detection. In: CVPR (2017)
Yu, Z., et al.: Simultaneous edge alignment and learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 400–417. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_24
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hatamizadeh, A., Terzopoulos, D., Myronenko, A. (2019). End-to-End Boundary Aware Networks for Medical Image Segmentation. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-32692-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32691-3
Online ISBN: 978-3-030-32692-0
eBook Packages: Computer ScienceComputer Science (R0)