Abstract
Gliomas are the most common primary brain malignancies. Identifying the sub-regions of gliomas before surgery is meaningful, which may extend the survival of patients. However, due to the heterogeneous appearance and shape of gliomas, it is a challenge to accurately segment the enhancing tumor, the necrotic, the non-enhancing tumor core and the peritumoral edema. In this study, an attention-based network was used to segment the glioma sub-regions in multi-modality MRI scans. Attention U-Net was employed as the basic architecture of the proposed network. The attention gates help the network focus on the task-relevant regions in the image. Besides the spatial-wise attention gates, the channel-wise attention gates proposed in SE Net were also embedded into the segmentation network. This attention mechanism in the feature dimension prompts the network to focus on the useful feature maps. Furthermore, in order to reduce false positives, a training strategy combined with a sampling strategy was proposed in our study. The segmentation performance of the proposed network was evaluated on the BraTS 2019 validation dataset and testing dataset. In the validation dataset, the dice similarity coefficients of enhancing tumor, tumor core and whole tumor were 0.759, 0.807 and 0.893 respectively. And in the testing dataset, the dice scores of enhancing tumor, tumor core and whole tumor were 0.794, 0.814 and 0.866 respectively.
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
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. 286 (2017)
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 170117 (2017)
Bakas, S., Reyes, M., Jakab, A., Bauer, S., et al.: Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge, arXiv preprint arXiv:1811.02629 (2019)
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Milletari, F., Navab, N., Ahmadi, S.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization, pp. 311–320 (2019)
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No New-Net, pp. 234–244 (2019)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, arXiv preprint arXiv:1810.04805 (2018)
Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., et al.: Attention U-Net: learning where to look for the pancreas, arXiv preprint arXiv:1804.03999 (2018)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Abadi, M., Barham, P., Chen, J., Chen, Z., et al.: Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), pp. 265–283 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, X., Zhao, W., Zhao, J. (2020). Brain Tumor Segmentation Using Attention-Based Network in 3D MRI Images. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-46643-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46642-8
Online ISBN: 978-3-030-46643-5
eBook Packages: Computer ScienceComputer Science (R0)
