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Unsupervised Anomaly Segmentation for Brain Lesions Using Dual Semantic-Manifold Reconstruction

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Neural Information Processing (ICONIP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13625))

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Abstract

Unsupervised anomaly segmentation (UAS) is promising in many computer vision applications, e.g., the analysis of brain MRI, thanks to the advantage of detecting the anomalies (lesions) by only using the normal samples (healthy anatomies) in the training phase. Existing methods utilize the reconstruction process to model the normative distribution but inevitably lead to the impairment of localization information, which is critical for the pixel-level detection task. In this paper, we address this challenge by formulating a semantic layout of the healthy anatomy as the reconstruction manifold, which naturally forces the embedding to explicitly encode more semantic features as well as facilitates the preservation of spatial information during the reconstruction. Based on this special autoencoder framework of Semantic-Manifold Reconstruction (SMR), we further apply two consistency regularizations not only on the semantic layout but also the image appearance. In this way a Dual Semantic-Manifold Reconstruction (DSMR) is trained and then used to detect the anomalies accurately. Experiments reveal that the proposed DSMR approach exceeds the state-of-the-art performance on the benchmark datasets of BraTS and ISLES.

The study is supported partly by the National Natural Science Foundation of China under Grants 82172033, U19B2031, 52105126, 82272071, 62271430, and 61971369.

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Notes

  1. 1.

    where Im denotes image and Se denotes semantic layout.

  2. 2.

    https://www.fil.ion.ucl.ac.uk/spm/software/download/.

  3. 3.

    https://github.com/StefanDenn3r/Unsupervised-Anomaly-Detection-Brain-MRI.

References

  1. Baur, C., Denner, S., Wiestler, B., Navab, N., Albarqouni, S.: Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study - science direct. Med. Image Anal. 69, 101952 (2021)

    Article  Google Scholar 

  2. Ashburner, J., Friston, K.J.: Computing average shaped tissue probability templates. Neuroimage 45(2), 333–341 (2009)

    Article  Google Scholar 

  3. Maier, O., et al.: ISLES 2015 - a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250–269 (2017)

    Article  Google Scholar 

  4. Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)

    Article  Google Scholar 

  5. Baur, C., Graf, R., Wiestler, B., Albarqouni, S., Navab, N.: SteGANomaly: inhibiting CycleGAN steganography for unsupervised anomaly detection in brain MRI. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 718–727. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59713-9_69

    Chapter  Google Scholar 

  6. Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Deep autoencoding models for unsupervised anomaly segmentation in brain MR images. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 161–169. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11723-8_16

    Chapter  Google Scholar 

  7. Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Scale-space autoencoders for unsupervised anomaly segmentation in brain MRI. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 552–561. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_54

    Chapter  Google Scholar 

  8. Chen, X., Konukoglu, E.: Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. arXiv preprint arXiv:1806.04972 (2018)

  9. Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A geometric framework for unsupervised anomaly detection. In: Barbará, D., Jajodia, S. (eds.) Applications of Data Mining in Computer Security. Advances in Information Security, vol. 6, pp. 77–101. Springer, Boston (2002). https://doi.org/10.1007/978-1-4615-0953-0_4

    Chapter  Google Scholar 

  10. Guthrie, D., Guthrie, L., Allison, B., Wilks, Y.: Unsupervised anomaly detection. In: International Joint Conferences on Artificial Intelligence (IJCAI), pp. 1624–1628 (2007)

    Google Scholar 

  11. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)

  13. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1125–1134 (2017)

    Google Scholar 

  14. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43

    Chapter  Google Scholar 

  15. Leung, K., Leckie, C.: Unsupervised anomaly detection in network intrusion detection using clusters. In: Proceedings of the Twenty-Eighth Australasian Conference on Computer Science, pp. 333–342 (2005)

    Google Scholar 

  16. Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)

    Article  Google Scholar 

  17. Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)

  18. Mohan, G., Subashini, M.M.: MRI based medical image analysis: survey on brain tumor grade classification. Biomed. Sign. Process. Control 39, 139–161 (2018)

    Article  Google Scholar 

  19. Müller, R., Kornblith, S., Hinton, G.E.: When does label smoothing help?. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 32 (2019)

    Google Scholar 

  20. Nguyen, B., Feldman, A., Bethapudi, S., Jennings, A., Willcocks, C.G.: Unsupervised region-based anomaly detection in brain MRI with adversarial image inpainting. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1127–1131. IEEE (2021)

    Google Scholar 

  21. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2337–2346 (2019)

    Google Scholar 

  22. Pawlowski, N., et al.: Unsupervised lesion detection in brain CT using Bayesian convolutional autoencoders. In: Medical Imaging with Deep Learning (2018)

    Google Scholar 

  23. Rezaei, M., Yang, H., Meinel, C.: Deep neural network with l2-norm unit for brain lesions detection. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) ICONIP 2017. Lecture Notes in Computer Science(), vol. 10637, pp. 798–807. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70093-9_85

    Chapter  Google Scholar 

  24. 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

    Chapter  Google Scholar 

  25. Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)

    Article  Google Scholar 

  26. Shen, Z., Liu, Z., Xu, D., Chen, Z., Cheng, K.T., Savvides, M.: Is label smoothing truly incompatible with knowledge distillation: An empirical study. In: Proceedings of the International Conference of Learning Representation (ICLR) (2020)

    Google Scholar 

  27. Taboada-Crispi, A., Sahli, H., Hernandez-Pacheco, D., Falcon-Ruiz, A.: Anomaly detection in medical image analysis. In: Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, pp. 426–446 (2009)

    Google Scholar 

  28. Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)

    Article  Google Scholar 

  29. You, S., Tezcan, K.C., Chen, X., Konukoglu, E.: Unsupervised lesion detection via image restoration with a normative prior. In: International Conference on Medical Imaging with Deep Learning (ICDCI), pp. 540–556 (2019)

    Google Scholar 

  30. Zimmerer, D., Isensee, F., Petersen, J., Kohl, S., Maier-Hein, K.: Unsupervised anomaly localization using variational auto-encoders. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 289–297 (2019)

    Google Scholar 

  31. Zimmerer, D., Kohl, S.A., Petersen, J., Isensee, F., Maier-Hein, K.H.: Context-encoding variational autoencoder for unsupervised anomaly detection. arXiv preprint arXiv:1812.05941 (2018)

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Authors and Affiliations

  1. School of Informatics, Xiamen University, Xiamen, China

    Zhiyuan Ding, Qi Dong, Haote Xu, Chenxin Li, Xinghao Ding & Yue Huang

  2. Institue of Artificial Intelligent, Xiamen University, Xiamen, China

    Xinghao Ding & Yue Huang

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  1. Zhiyuan Ding
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  5. Xinghao Ding
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Correspondence to Xinghao Ding .

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Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Ding, Z., Dong, Q., Xu, H., Li, C., Ding, X., Huang, Y. (2023). Unsupervised Anomaly Segmentation for Brain Lesions Using Dual Semantic-Manifold Reconstruction. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_12

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  • DOI: https://doi.org/10.1007/978-3-031-30111-7_12

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