Abstract
Gliomas are the most common brain tumors, which have a high mortality. Magnetic resonance imaging (MRI) is useful to assess gliomas, in which segmentation of multimodal brain tissues in 3D medical images is of great significance for brain diagnosis. Due to manual job for segmentation is time-consuming, an automated and accurate segmentation method is required. How to segment multimodal brain accurately is still a challenging task. To address this problem, we employ residual neural blocks and a U-Net architecture to build a novel network. We have evaluated the performances of different primary residual neural blocks in building U-Net. Our proposed method was evaluated on the validation set of BraTS 2020, in which our model makes an effective segmentation for the complete, core and enhancing tumor regions in Dice Similarity Coefficient (DSC) metric (0.89, 0.78, 0.72). And in testing set, our model got the DSC results of 0.87, 0.82, 0.80. Residual convolutional block is especially useful to improve performance in building model. Our proposed method is inherently general and is a powerful tool to studies of medical images of brain tumors.
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., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. 286 (2017)
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection, July 2017. https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)
Bakas, 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 (2018)
Bauer, S., Nolte, L.-P., Reyes, M.: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23626-6_44
Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97 (2013)
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.R., 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
Ding, Y., Gong, L., Zhang, M., Li, C., Qin, Z.: A multi-path adaptive fusion network for multimodal brain tumor segmentation. Neurocomputing 412, 19–30 (2020)
Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 506–517. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_44
Havaei, M., Jodoin, P.M., Larochelle, H.: Efficient interactive brain tumor segmentation as within-brain KNN classification. In: 2014 22nd International Conference on Pattern Recognition, pp. 556–561. IEEE (2014)
Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recogn. 38(12), 2270–2285 (2005)
Lee, C.-H., Wang, S., Murtha, A., Brown, M.R.G., Greiner, R.: Segmenting brain tumors using pseudo–conditional random fields. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5241, pp. 359–366. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85988-8_43
Li, J., Yu, Z.L., Gu, Z., Liu, H., Li, Y.: MMAN: multi-modality aggregation network for brain segmentation from MR images. Neurocomputing 358, 10–19 (2019)
Liu, L., Cheng, J., Quan, Q., Wu, F.X., Wang, Y.P., Wang, J.: A survey on U-shaped networks in medical image segmentations. Neurocomputing 409, 244–258 (2020)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Naser, M.A., Deen, M.J.: Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput. Biol. Med. 121, 103758 (2020)
Noori, M., Bahri, A., Mohammadi, K.: Attention-guided version of 2D UNet for automatic brain tumor segmentation. In: 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 269–275. IEEE (2019)
Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)
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
Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853 (2015)
Xu, F., Ma, H., Sun, J., Wu, R., Liu, X., Kong, Y.: LSTM multi-modal UNet for brain tumor segmentation. In: 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), pp. 236–240. IEEE (2019)
Acknowledgement
This work was supported in part by National Natural Science Foundation of China under Grant No. 61976126, Shandong Natural Science Foundation under Grant No. ZR2019MF003, No. ZR2017MF054.
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
Sun, J., Peng, Y., Li, D., Guo, Y. (2021). Segmentation of the Multimodal Brain Tumor Images Used Res-U-Net. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-72084-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72083-4
Online ISBN: 978-3-030-72084-1
eBook Packages: Computer ScienceComputer Science (R0)
