Abstract
Gliomas are the most common and aggressive malignant primary brain tumors. Automatic brain tumor segmentation from multi-modality magnetic resonance images using deep learning methods is critical for gliomas diagnosis. Deep learning segmentation architectures, especially based on fully convolutional neural network, have proved great performance on medical image segmentation. However, these approaches cannot explicitly model global information and overlook the topology structure of lesion regions, which leaves room for improvement. In this paper, we propose a convolution-and-transformer network (COTRNet) to explicitly capture global information and a topology aware loss to constrain the network to learn topological information. Moreover, we exploit transfer learning by using pretrained parameters on ImageNet and deep supervision by adding multi-level predictions to further improve the segmentation performance. COTRNet achieved dice scores of \(78.08\%\), \(76.18\%\), and \(83.92\%\) in the enhancing tumor, the tumor core, and the whole tumor segmentation on brain tumor segmentation challenge 2021. Experimental results demonstrated effectiveness of the proposed method.
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References
Ahmad, P., Qamar, S., Shen, L., Saeed, A.: Context Aware 3D UNet for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12658, pp. 207–218. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72084-1_19
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the tcga-gbm collection. the cancer imaging archive. Nat Sci Data 4, 170117 (2017)
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection. The cancer imaging archive 286 (2017)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 1–13 (2017)
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13
Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
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
Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Artificial Intelligence and Statistics, pp. 562–570. PMLR (2015)
Liu, C., Ding, W., Li, L., Zhang, Z., Pei, C., Huang, L., Zhuang, X.: Brain tumor segmentation network using attention-based fusion and spatial relationship constraint. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12658, pp. 219–229. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72084-1_20
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Louis, D.N., et al.: The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131(6), 803–820 (2016)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural. Inf. Process. Syst. 32, 8026–8037 (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
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wacker, J., Ladeira, M., Nascimento, J.E.V.: Transfer learning for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12658, pp. 241–251. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72084-1_22
Wang, Z., Zou, N., Shen, D., Ji, S.: Non-local u-nets for biomedical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 6315–6322 (2020)
Zhang, Y., Liu, H., Hu, Q.: Transfuse: fusing transformers and CNNs for medical image segmentation. arXiv preprint arXiv:2102.08005 (2021)
Zheng, S., et al.: A dual-attention V-network for pulmonary lobe segmentation in CT scans. IET Image Proc. 15(8), 1644–1654 (2021)
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Yang, H., Shen, Z., Li, Z., Liu, J., Xiao, J. (2022). Combining Global Information with Topological Prior for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12962. Springer, Cham. https://doi.org/10.1007/978-3-031-08999-2_16
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