Abstract
A cascaded framework of coarse-to-fine networks is proposed to segment brain tumor from multi-modality MR images into three subregions: enhancing tumor, whole tumor and tumor core. The framework is designed to decompose this multi-class segmentation into two sequential tasks according to hierarchical relationship among these regions. In the first task, a coarse-to-fine model based on Global Context Network predicts segmentation of whole tumor, which provides a bounding box of all three substructures to crop the input MR images. In the second task, cropped multi-modality MR images are fed into another two coarse-to-fine models based on NvNet trained on small patches to generate segmentation of tumor core and enhancing tumor, respectively. Experiments with BraTS 2020 validation set show that the proposed method achieves average Dice scores of 0.8003, 0.9123, 0.8630 for enhancing tumor, whole tumor and tumor core, respectively. The corresponding values for BraTS 2020 testing set were 0.81715, 0.88229, 0.83085, 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
Similar content being viewed by others
References
Bakas, S., Akbari, H., Sotiras, A., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(170117) (2017). https://doi.org/10.1038/sdata.2017.117
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collectio, July 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, July 2017. https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
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. CoRR abs/1811.02629 (2018). http://arxiv.org/abs/1811.02629
Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: Gcnet: non-local networks meet squeeze-excitation networks and beyond. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 1971–1980 (2019)
Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biome. Eng. 19, 221–248 (2017)
Guo, D., Wang, L., Song, T., Wang, G.: Cascaded global context convolutional neural network for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 315–326. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46640-4_30
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 234–244. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_21
Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded U-Net: 1st place solution to BraTS challenge 2019 segmentation task. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 231–241. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46640-4_22
Kamnitsas, K., et al.: Deepmedic for brain tumor segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 138–149. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-55524-9_14
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, December 2014
Lingyun, W., Yang, X., Li, S., Wang, T., Heng, P.A., Ni, D.: Cascaded fully convolutional networks for automatic prenatal ultrasound image segmentation, pp. 663–666, April 2017. https://doi.org/10.1109/ISBI.2017.7950607
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
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
Paszke, A., et al.: Automatic differentiation in pytorch (2017)
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
Roth, H.R., et al.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_68
Sevastopolsky, A.: Optic disc and cup segmentation methods for glaucoma detection with modification of u-net convolutional neural network. Pattern Recognit. Image Anal. 27, 618–624 (2017). https://doi.org/10.1134/S1054661817030269
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)
Siskind, J., Pearlmutter, B.: Divide-and-conquer checkpointing for arbitrary programs with no user annotation. Optim. Methods Softw. 33 (2017). https://doi.org/10.1080/10556788.2018.1459621
Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T.: Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338, 34–45 (2019). https://doi.org/10.1016/j.neucom.2019.01.103
Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 178–190. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_16
Wang, G., Song, T., Dong, Q., Cui, M., Huang, N., Zhang, S.: Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Med. Image Anal. 65, 101787 (2020). http://www.sciencedirect.com/science/article/pii/S1361841520301511
Wong, K.C.L., Moradi, M., Tang, H., Syeda-Mahmood, T.: 3D segmentation with exponential logarithmic loss for highly unbalanced object sizes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 612–619. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_70
Zhao, Y.-X., Zhang, Y.-M., Liu, C.-L.: Bag of tricks for 3D MRI brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 210–220. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46640-4_20
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
Yang, S., Guo, D., Wang, L., Wang, G. (2021). Cascaded Coarse-to-Fine Neural Network for Brain Tumor Segmentation. 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_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-72084-1_41
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)
