Abstract
CNNs represent the current state of the art for image classification, as well as for image segmentation. Recent work suggests that CNNs for image classification suffer from a bias towards texture, and that reducing it can increase the network’s accuracy. We hypothesize that CNNs for medical image segmentation might suffer from a similar bias. We propose to reduce it by augmenting the training data with feature preserving smoothing, which reduces noise and high-frequency textural features, while preserving semantically meaningful boundaries. Experiments on multiple medical image segmentation tasks confirm that, especially when limited training data is available or a domain shift is involved, feature preserving smoothing can indeed serve as a simple and effective augmentation technique.
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References
Andreu, F., et al.: Minimizing total variation flow. Diff. Integral Eq. 14(3), 321–360 (2001)
Aurich, V., Weule, J.: Non-linear Gaussian filters performing edge preserving diffusion. In: Sagerer, G., Posch, S., Kummert, F. (eds.) Mustererkennung. Informatik Aktuell, pp. 538–545. Springer, Heidelberg (1995). https://doi.org/10.1007/978-3-642-79980-8_63
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. Technical report 1811.02629, arXiv (2018)
Billot, B., Greve, D., Van Leemput, K., Fischl, B., Iglesias, J.E., Dalca, A.V.: A learning strategy for contrast-agnostic MRI segmentation. Technical report 2003.01995, arXiv (2020)
Bowles, C., et al.: GAN augmentation: augmenting training data using generative adversarial networks. Technical report 1810.10863, arXiv (2018)
Chaitanya, K., Karani, N., Baumgartner, C.F., Becker, A., Donati, O., Konukoglu, E.: Semi-supervised and task-driven data augmentation. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 29–41. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_3
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414–2423 (2016)
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: International Conference on Learning Representations (ICLR) (2019)
He, K., Sun, J., Tang, X.: Guided image filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_1
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 287–297. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_25
Jackson, P.T.G., Abarghouei, A.A., Bonner, S., Breckon, T.P., Obara, B.: Style augmentation: Data augmentation via style randomization. In: CVPR Deep Vision Workshop, pp. 83–92 (2019)
Kuijf, H.J., et al.: Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge. IEEE Trans. Med. Imaging 38(11), 2556–2568 (2019)
Li, H., et al.: Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images. NeuroImage 183, 650–665 (2018)
Ma, R., Tao, P., Tang, H.: Optimizing data augmentation for semantic segmentation on small-scale dataset. In: Proceedings of International Conference on Control and Computer Vision (ICCCV), pp. 77–81 (2019)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Perone, C.S., Ballester, P., Barros, R.C., Cohen-Adad, J.: Unsupervised domain adaptation for medical imaging segmentation with self-ensembling. NeuroImage 194, 1–11 (2019)
Prados, F., et al.: Spinal cord grey matter segmentation challenge. NeuroImage 152, 312–329 (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
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1), 259–268 (1992)
Sandfort, V., Yan, K., Pickhardt, P.J., Summers, R.M.: Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Sci. Rep. 9(1), 16884 (2019)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019)
Weickert, J., Romeny, B.T.H., Viergever, M.: Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans. Image Process. 7(3), 398–410 (1998)
Zhang, Y., et al.: SPDA: superpixel-based data augmentation for biomedical image segmentation. In: International Conference on Medical Imaging with Deep Learning (MIDL). Proceedings of Machine Learning Research, vol. 102, pp. 572–587 (2019)
Zhao, A., Balakrishnan, G., Durand, F., Guttag, J.V., Dalca, A.V.: Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8543–8553 (2019)
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Sheikh, R., Schultz, T. (2020). Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12261. Springer, Cham. https://doi.org/10.1007/978-3-030-59710-8_12
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DOI: https://doi.org/10.1007/978-3-030-59710-8_12
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