Abstract
Gliomas are one of the most widespread and aggressive forms of brain tumors. Accurate brain tumor segmentation is crucial for evaluation, monitoring and treatment of gliomas. Recent advances in deep learning methods have made a significant step towards a robust and automated brain tumor segmentation. However, due to the variation in shape and location of gliomas, as well as their appearance across different tumor grades, obtaining an accurate and generalizable segmentation model is still a challenge. To alleviate this, we propose a cascaded segmentation pipeline, aimed at introducing more robustness to segmentation performance through data stratification. In other words, we train separate models per tumor grade, aided with synthetic brain tumor images generated through conditional generative adversarial networks. To handle the variety in size, shape and location of tumors, we utilize a localization module, focusing the training and inference in the vicinity of the tumor. Finally, to identify which tumor grade segmentation model to utilize at inference time, we train a dense, attention-based 3D classification model. The obtained results suggest that both stratification and the addition of synthetic data to training significantly improve the segmentation performance, whereby up to 55% of test cases exhibit a performance improvement by more than 5% and up to 40% of test cases exhibit an improvement by more than 10% in Dice score.
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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)
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., 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)
Brosch, T., Saalbach, A.: Foveal fully convolutional nets for multi-organ segmentation. In: Medical Imaging 2018: Image Processing, vol. 10574, p. 105740U. International Society for Optics and Photonics (2018)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2337–2346 (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)
Rebsamen, M., Knecht, U., Reyes, M., Wiest, R., Meier, R., McKinley, R.: Divide and conquer: stratifying training data by tumor grade improves deep learning-based brain tumor segmentation. Front. Neurosci. 13, 1182 (2019)
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
Zhang, J., Zheng, B., Gao, A., Feng, X., Liang, D., Long, X.: A 3d densely connected convolution neural network with connection-wise attention mechanism for Alzheimer’s disease classification. Magn. Reson. Imaging 78, 119–126 (2021)
Acknowledgements
This research is part of the openGTN project, supported by the European Union in the Marie Curie Innovative Training Networks (ITN) fellowship program under project No. 764465.
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Khalil, Y.A., Ayaz, A., Lorenz, C., Weese, J., Pluim, J., Breeuwer, M. (2022). A Stratified Cascaded Approach for Brain Tumor Segmentation with the Aid of Multi-modal Synthetic Data. In: Nguyen, H.V., Huang, S.X., Xue, Y. (eds) Data Augmentation, Labelling, and Imperfections. DALI 2022. Lecture Notes in Computer Science, vol 13567. Springer, Cham. https://doi.org/10.1007/978-3-031-17027-0_10
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