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2020 | OriginalPaper | Buchkapitel

Anatomical Data Augmentation via Fluid-Based Image Registration

verfasst von : Zhengyang Shen, Zhenlin Xu, Sahin Olut, Marc Niethammer

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Verlag: Springer International Publishing

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Abstract

We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our approach consists of three steps: 1) given a source image and a set of target images, we construct a geodesic subspace using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model; 2) we sample transformations from the resulting geodesic subspace; 3) we obtain deformed images and segmentations via interpolation. Experiments on brain (LPBA) and knee (OAI) data illustrate the performance of our approach on two tasks: 1) data augmentation during training and testing for image segmentation; 2) one-shot learning for single atlas image segmentation. We demonstrate that our approach generates anatomically meaningful data and improves performance on these tasks over competing approaches. Code is available at https://github.com/uncbiag/easyreg.

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Vorheriges Kapitel Fluid Registration Between Lung CT and Stationary Chest Tomosynthesis Images

Nächstes Kapitel Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration

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In some cases, for example for lung images, sliding effects need to be considered, violating the diffeomorphic assumption.

Aljabar, P., Wolz, R., Rueckert, D.: Manifold learning for medical image registration, segmentation, and classification. In: Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, pp. 351–372. IGI Global (2012)

Ambellan, F., Tack, A., Ehlke, M., Zachow, S.: Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: data from the osteoarthritis initiative. Med. Image Anal. 52, 109–118 (2019)CrossRef

Bai, W., et al.: Semi-supervised learning for network-based cardiac MR image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 253–260. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_29CrossRef

Baur, C., Albarqouni, S., Navab, N.: Semi-supervised deep learning for fully convolutional networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 311–319. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_36CrossRef

Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. IJCV 61(2), 139–157 (2005)CrossRef

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_3CrossRef

Chen, S., Bortsova, G., García-Uceda Juárez, A., van Tulder, G., de Bruijne, M.: Multi-task attention-based semi-supervised learning for medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 457–465. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_51CrossRef

Dupuis, P., Grenander, U., Miller, M.I.: Variational problems on flows of diffeomorphisms for image matching. Q. Appl. Math. LVI, 587–600 (1998)MathSciNetCrossRef

Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321–331 (2018)CrossRef

10.

Hauberg, S., Freifeld, O., Larsen, A.B.L., Fisher, J., Hansen, L.: Dreaming more data: class-dependent distributions over diffeomorphisms for learned data augmentation. In: Artificial Intelligence and Statistics, pp. 342–350 (2016)

11.

Heinrich, M.P., Oktay, O., Bouteldja, N.: Obelisk-one kernel to solve nearly everything: unified 3D binary convolutions for image analysis (2018)

12.

Hussain, Z., Gimenez, F., Yi, D., Rubin, D.: Differential data augmentation techniques for medical imaging classification tasks. In: AMIA Annual Symposium Proceedings, vol. 2017, p. 979. American Medical Informatics Association (2017)

13.

Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, S151–S160 (2004)CrossRef

14.

Learned-Miller, E.G.: Data driven image models through continuous joint alignment. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 236–250 (2005)CrossRef

15.

Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)

16.

Nie, D., Gao, Y., Wang, L., Shen, D.: ASDNet: attention based semi-supervised deep networks for medical image segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 370–378. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_43CrossRef

17.

Niethammer, M., Kwitt, R., Vialard, F.X.: Metric learning for image registration. In: CVPR (2019)

18.

Oliveira, A., Pereira, S., Silva, C.A.: Augmenting data when training a CNN for retinal vessel segmentation: how to warp? In: 2017 IEEE 5th Portuguese Meeting on Bioengineering (ENBENG), pp. 1–4. IEEE (2017)

19.

Park, S., Thorpe, M.: Representing and learning high dimensional data with the optimal transport map from a probabilistic viewpoint. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7864–7872 (2018)

20.

Paschali, M., et al.: Data augmentation with manifold exploring geometric transformations for increased performance and robustness. arXiv preprint arXiv:1901.04420 (2019)

21.

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)CrossRef

22.

Qiu, A., Younes, L., Miller, M.I.: Principal component based diffeomorphic surface mapping. IEEE Trans. Med. Imaging 31(2), 302–311 (2011)

23.

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_28CrossRef

24.

Roth, H.R., et al.: Anatomy-specific classification of medical images using deep convolutional nets. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 101–104. IEEE (2015)

25.

Shattuck, D.W., et al.: Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39(3), 1064–1080 (2008)CrossRef

26.

Shen, Z., Han, X., Xu, Z., Niethammer, M.: Networks for joint affine and non-parametric image registration. In: CVPR (2019)

27.

Shen, Z., Vialard, F.X., Niethammer, M.: Region-specific diffeomorphic metric mapping. In: Advances in Neural Information Processing Systems, pp. 1096–1106 (2019)

28.

Shin, H.C., et al.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: Gooya, A., Goksel, O., Oguz, I., Burgos, N. (eds.) SASHIMI 2018. LNCS, vol. 11037, pp. 1–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00536-8_1CrossRef

29.

The Osteoarthritis Initiative: Osteoarthritis initiative (OAI) dataset. https://nda.nih.gov/oai/

30.

Vakalopoulou, M., et al.: AtlasNet: multi-atlas non-linear deep networks for medical image segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 658–666. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_75CrossRef

31.

Xu, Z., Niethammer, M.: DeepAtlas: joint semi-supervised learning of image registration and segmentation. arXiv preprint arXiv:1904.08465 (2019)

32.

Xu, Z., Shen, Z., Niethammer, M.: Contextual additive networks to efficiently boost 3D image segmentations. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 92–100. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_11CrossRef

33.

Younes, L., Arrate, F., Miller, M.I.: Evolutions equations in computational anatomy. NeuroImage 45(1), S40–S50 (2009)CrossRef

34.

Zhang, M., Singh, N., Fletcher, P.T.: Bayesian estimation of regularization and atlas building in diffeomorphic image registration. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 37–48. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38868-2_4CrossRef

35.

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 the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8543–8553 (2019)

36.

Zhou, Y., et al.: Collaborative learning of semi-supervised segmentation and classification for medical images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2079–2088 (2019)

Titel: Anatomical Data Augmentation via Fluid-Based Image Registration
verfasst von: Zhengyang Shen
Zhenlin Xu
Sahin Olut
Marc Niethammer
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Print ISBN: 978-3-030-59715-3

Electronic ISBN: 978-3-030-59716-0

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-59716-0_31

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