Abstract
In this paper, we propose an automated three dimensional (3D) deep learning approach for the segmentation of gliomas in pre-operative brain MRI scans. We introduce a state-of-the-art multi-resolution architecture based on encoder-decoder which comprise of separate branches to incorporate local high-resolution image features and wider low-resolution contextual information. We also used a unified multi-task loss function to provide end-to-end segmentation training. For the task of survival prediction, we propose a regression algorithm based on random forests to predict the survival days for the patients. Our proposed network is fully automated and designed to take input as patches that can work on input images of any arbitrary size. We trained our proposed network on the BraTS 2020 challenge dataset that consists of 369 training cases, and then validated on 125 unseen validation datasets, and tested on 166 unseen cases from the testing dataset using a blind testing approach. The quantitative and qualitative results demonstrate that our proposed network provides efficient segmentation of brain tumors. The mean Dice overlap measures for automatic brain tumor segmentation of the validation dataset against ground truth are 0.87, 0.80, and 0.66 for the whole tumor, core, and enhancing tumor, respectively. The corresponding results for the testing dataset are 0.78, 0.70, and 0.66, respectively. The accuracy measures of the proposed model for the survival prediction tasks are 0.45 and 0.505 for the validation and testing datasets, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gordillo, N., Montseny, E., Sobrevilla, P.: State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging 31, 1426–1438 (2013)
Soltaninejad, M., Zhang, L., Lambrou, T., Yang, G., Allinson, N., Ye, X.: MRI brain tumor segmentation and patient survival prediction using random forests and fully convolutional networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 204–215. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_18
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
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
Feng, X., Tustison, N., Meyer, C.: Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 279–288. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_25
Chen, W., Liu, B., Peng, S., Sun, J., Qiao, X.: S3D-UNet: separable 3D U-net for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 358–368. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_32
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29
Soltaninejad, M., Sturrock, C.J., Griffiths, M., Pridmore, T.P., Pound, M.P.: Three dimensional root CT segmentation using multi-resolution encoder-decoder networks. IEEE Trans. Image Process. 29, 6667–6679 (2020)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015). https://doi.org/10.1109/TMI.2014.2377694
Bakas, S., et al.: Advancing The cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 170117 (2017). https://doi.org/10.1038/sdata.2017.117
Bakas, S., et al.: Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (2018). arXiv preprint arXiv:1811.02629
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imag. Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imag. Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)
Paszke, A., et al.: Automatic differentiation in pytorch (2017)
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
Soltaninejad, M., Pridmore, T., Pound, M. (2021). Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder Networks. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-72087-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72086-5
Online ISBN: 978-3-030-72087-2
eBook Packages: Computer ScienceComputer Science (R0)