Abstract
Accurate segmentation of brain tumors from the magnetic resonance image (MRI) is an essential step for radionics analysis as well as finding the tumor extension is so necessary to plan the best treatment to improve the survival rate. Manually extracting sub-regions of the brain tumor from MRI is a tedious process and time-consuming, as the complex brain tumor images require extensive human expertise. In recent years, deep learning models have proved effective in medical image segmentation tasks. In brain tumor segmentation, the 3D multimodal MRI poses some challenges such as computation and memory limitations. This study aims to develop a deep learning model using 3D U-Net for brain tumor segmentation. The segmentation results on BraTS 2020 dataset show that the proposed model achieves promising performance.
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
Similar content being viewed by others
References
Quick brain tumor facts. https://braintumor.org/brain-tumor-information/brain-tumor-facts/. Accessed 23 Mar 2021
Khalil, H.A., Darwish, S., Ibrahim, Y.M., Hassan, O.F.: 3D-MRI brain tumor detection model using modified version of level set segmentation based on dragonfly algorithm. Symmetry 12(8), 1256 (2020)
Glioma. https://www.cancerresearchuk.org/about-cancer/brain-tumours/types/glioma-adults. Accessed 23 Mar 2021
Glioma. https://rarediseases.org/rare-diseases/glioma/. Accessed 23 Mar 2021
Banerjee, S., Mitra, S.: Novel volumetric sub-region segmentation in brain tumors. Front. Comput. Neurosci. 14, 3 (2020)
Sun, L., Zhang, S., Chen, H., Luo, L.: Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Front. Neurosci. 13, 810 (2019)
Qamar, S., Ahmad, P., Shen, L.: HI-Net: hyperdense inception 3D UNet for brain tumor segmentation. arXiv Preprint (2020). arXiv:2012.06760
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv Preprint (2018). arXiv:1811.02629
Feng, X., Tustison, N.J., Patel, S.H., Meyer, C.H.: Brain tumor segmentation using an ensemble of 3d u-nets and overall survival prediction using radiomic features. Front. Comput. Neurosci. 14, 25 (2020)
Srinivas, B., Sasibhushana Rao, G.: Segmentation of multi-modal MRI brain tumor sub-regions using deep learning. J. Electr. Eng. Technol. 15(4), 1899–1909 (2020). https://doi.org/10.1007/s42835-020-00448-z
Chen, W., Liu, B., Peng, S., Sun, J., Qiao, X.: S3D-UNet: separable 3D U-Net for brain tumor segmentation. In: International MICCAI Brainlesion Workshop, pp. 358–368. Springer, Cham (2018)
Zhao, W., Jiang, D., Queralta, J.P., Westerlund, T.: MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net. Inf. Med. Unlocked 19, 100357 (2020)
Ali, A.A., Chramcov, B., Jasek, R., Katta, R., Krayem, S.: Classification of plant diseases using convolutional neural networks. In: Silhavy, R. (eds.) Artificial Intelligence in Intelligent Systems. CSOC 2021. Lecture Notes in Networks and Systems, vol. 229. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77445-5-24
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 424–432. Springer, Cham (2016)
Ali, A.A., Chramcov, B., Jasek, R., Katta, R., Krayem, S., Kadi, M.: Detection of steel surface defects using U-Net with pre-trained encoder. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) Software Engineering Application in Informatics. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol. 232. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90318-3-18
Ibtehaz, N., Rahman, M.S.: MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020)
Li, C., Chen, W., Tan, Y.: Point-Sampling method based on 3D U-Net architecture to reduce the influence of false positive and solve boundary blur problem in 3D CT image segmentation. Appl. Sci. 10(19), 6838 (2020)
Ngo, D.K., Tran, M.T., Kim, S.H., Yang, H.J., Lee, G.S.: Multi-Task learning for small brain tumor segmentation from MRI. Appl. Sci. 10(21), 7790 (2020)
BraTS2020 Dataset. https://www.kaggle.com/awsaf49/brats20-dataset-training-validation. Accessed 23 Mar 2021
Multimodal brain tumor segmentation challenge 2020: data. https://www.med.upenn.edu/cbica/brats2020/data.html. Accessed 23 Mar 2021
Wu, Y., He, K.: Group normalization. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
https://pytorch.org/docs/stable/generated/torch.nn.MaxPool3d.html
Ho, Y., Wookey, S.: The real-world-weight cross-entropy loss function: modeling the costs of mislabeling. IEEE Access 8, 4806–4813 (2020). https://doi.org/10.1109/ACCESS.2019.2962617
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization (2015). Accessed 13 Mar 2021. (Online). Available:https://arxiv.org/abs/1412.6980v9
NVIDIA® TESLA® P100 GPU accelerator. https://images.nvidia.com/content/tesla/pdf/nvidia-tesla-p100-PCIe-datasheet.pdf. Accessed 16 Jan 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ali, A.A., Katta, R., Jasek, R., Chramco, B., Krayem, S. (2023). Sub-region Segmentation of Brain Tumors from Multimodal MRI Images Using 3D U-Net. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Algorithms in Systems. CoMeSySo 2022. Lecture Notes in Networks and Systems, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-031-21438-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-031-21438-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21437-0
Online ISBN: 978-3-031-21438-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)