Abstract
In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images. Our work challenges the effectiveness of current Machine Learning (ML) approaches in this application domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR) MR scans provides better anomaly segmentation maps than several different ML-based anomaly detection models. Specifically, our method achieves better Dice similarity coefficients and Precision-Recall curves than the competitors on various popular evaluation data sets for the segmentation of tumors and multiple sclerosis lesions. (Code available under: https://github.com/FeliMe/brain_sas_baseline)
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Atlason, H.E., Love, A., Sigurdsson, S., Gudnason, V., Ellingsen, L.M.: Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder. In: Medical Imaging 2019: Image Processing, March 2019. https://doi.org/10.1117/12.2512953
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 2052–4463 (2017). https://doi.org/10.1038/sdata.2017.117
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge (2019)
Baur, C., Denner, S., Wiestler, B., Navab, N., Albarqouni, S.: Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med. Image Anal. 69, 101952 (2021). https://doi.org/10.1016/j.media.2020.101952. https://www.sciencedirect.com/science/article/pii/S1361841520303169
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
Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Bayesian skip-autoencoders for unsupervised hyperintense anomaly detection in high resolution brain MRI. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1905–1909 (2020). https://doi.org/10.1109/ISBI45749.2020.9098686
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
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD - a comprehensive real-world dataset for unsupervised anomaly detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9584–9592 (2019). https://doi.org/10.1109/CVPR.2019.00982
Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., Steger, C.: Improving unsupervised defect segmentation by applying structural similarity to autoencoders. In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2019). https://doi.org/10.5220/0007364503720380
Carass, A., et al.: Longitudinal multiple sclerosis lesion segmentation: resource and challenge. NeuroImage 148, 77–102 (2017). https://doi.org/10.1016/j.neuroimage.2016.12.064, https://www.sciencedirect.com/science/article/pii/S1053811916307819
Chen, X., Konukoglu, E.: Unsupervised detection of lesions in brain MRI using constrained adversarial auto-encoders. In: MIDL 2018 Conference book. MIDL, July 2018. https://doi.org/10.3929/ethz-b-000321650
Chen, X., You, S., Tezcan, K.C., Konukoglu, E.: Unsupervised lesion detection via image restoration with a normative prior. Med. Image Anal. 64, 101713 (2020). https://doi.org/10.1016/j.media.2020.101713. https://www.sciencedirect.com/science/article/pii/S1361841520300773
Dehaene, D., Frigo, O., Combrexelle, S., Eline, P.: Iterative energy-based projection on a normal data manifold for anomaly localization. In: International Conference on Learning Representations (2020)
Iglesias, J.E., Liu, C.Y., Thompson, P.M., Tu, Z.: Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans. Med. Imaging 30, 1617–1634 (2011). https://doi.org/10.1109/TMI.2011.2138152
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). https://doi.org/10.1109/tmi.2019.2905770
Lesjak, Ž, et al.: A novel public MR image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus. Neuroinformatics 16(1), 51–63 (2017). https://doi.org/10.1007/s12021-017-9348-7
Liu, W., et al.: Towards visually explaining variational autoencoders. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8639–8648 (2020). https://doi.org/10.1109/CVPR42600.2020.00867
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015). https://doi.org/10.1109/TMI.2014.2377694
Pawlowski, N., et al.: Unsupervised lesion detection in brain CT using Bayesian convolutional autoencoders. In: MIDL 2018 Conference Book. MIDL, April 2018
Pinaya, W.H.L., et al.: Unsupervised brain anomaly detection and segmentation with transformers (2021)
Rohlfing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31, 798–819 (2010). https://doi.org/10.1002/hbm.20906
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). https://doi.org/10.1016/j.media.2019.01.010
vanHespen, K.M., Zwanenburg, J.J.M., Dankbaar, J.W., Geerlings, M.I., Hendrikse, J., Kuijf, H.J.: An anomaly detection approach to identify chronic brain infarcts on MRI. Sci. Rep. 11, 7714 (2021). https://doi.org/10.1038/s41598-021-87013-4. https://doi.org/10.1038/s41598-021-87013-4
van der Walt, S., et al.: The scikit-image contributors: scikit-image: image processing in Python. PeerJ 2, e453 (2014). https://doi.org/10.7717/peerj.453
Yi, J., Yoon, S.: Patch SVDD: patch-level SVDD for anomaly detection and segmentation. In: Proceedings of the Asian Conference on Computer Vision (ACCV), November 2020
Zimmerer, D., Isensee, F., Petersen, J., Kohl, S., Maier-Hein, K.: Unsupervised anomaly localization using variational auto-encoders. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 289–297. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_32
Zimmerer, D., Petersen, J., Isensee, F., Maier-Hein, K.: Context-encoding variational autoencoder for unsupervised anomaly detection. In: International Conference on Medical Imaging with Deep Learning - Extended Abstract Track. London, United Kingdom, 08–10 July 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Meissen, F., Kaissis, G., Rueckert, D. (2022). Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI. 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_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-08999-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08998-5
Online ISBN: 978-3-031-08999-2
eBook Packages: Computer ScienceComputer Science (R0)