Abstract
Unsupervised methods for anomaly segmentation are promising for computer-aided diagnosis since they can increase the robustness of medical systems and do not require large annotated datasets. In this work, we propose a simple yet effective two-stage pipeline for improving the performance of existing anomaly segmentation methods. The first stage is used for better anomaly localization and false positive rate reduction. For this stage, we propose the PatchCore3D method, which is based on the PatchCore algorithm and a backbone, pre-trained on 3D medical images. Any existing anomaly segmentation method can be used at the second stage for the precise anomaly segmentation in the region suggested by PatchCore3D. We evaluate PatchCore3D and the proposed pipelines in combination with six top-performing anomaly segmentation methods of different types. We use brain MRI datasets, testing healthy subjects against subjects with brain tumors. Using PatchCore3D pipeline with every considered anomaly segmentation method increases segmentation AUROC almost twice by better anomaly localization.
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References
Agarwal, P.K., Har-Peled, S., Varadarajan, K.R., et al.: Geometric approximation via coresets. Comb. Comput. Geom. 52(1), 1–30 (2005)
Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)
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)
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)
Behrendt, F., Bengs, M., Rogge, F., Krüger, J., Opfer, R., Schlaefer, A.: Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). pp. 1–4. IEEE (2022)
Bengs, M., Behrendt, F., Krüger, J., Opfer, R., Schlaefer, A.: 3-Dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI. arXiv preprint arXiv:2109.06540 (2021)
Bercea, C.I., Wiestler, B., Rueckert, D., Schnabel, J.A.: Reversing the abnormal: Pseudo-healthy generative networks for anomaly detection. arXiv preprint arXiv:2303.08452 (2023)
Chan, H.P., Hadjiiski, L.M., Samala, R.K.: Computer-aided diagnosis in the era of deep learning. Med. Phys. 47(5), e218–e227 (2020)
Dufumier, B., et al.: Contrastive learning with continuous proxy meta-data for 3D MRI classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 58–68. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_6
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708 (2017)
Iqbal, H., Khalid, U., Hua, J., Chen, C.: Unsupervised anomaly detection in medical images using masked diffusion model. arXiv preprint arXiv:2305.19867 (2023)
Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with gpus. IEEE Trans. Big Data 7(3), 535–547 (2019)
Kascenas, A., Young, R., Jensen, B.S., Pugeault, N., O’Neil, A.Q.: Anomaly Detection via Context and Local Feature Matching. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5. IEEE (2022)
Marimont, S.N., Tarroni, G.: Anomaly detection through latent space restoration using vector quantized variational autoencoders. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1764–1767. IEEE (2021)
Meissen, F., Kaissis, G., Rueckert, D.: Challenging current semi-supervised anomaly segmentation methods for brain MRI. In: International MICCAI brainlesion workshop, pp. 63–74. Springer (2022). https://doi.org/10.1007/978-3-031-08999-2_5
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Naval Marimont, S., Tarroni, G.: Implicit Field Learning for Unsupervised Anomaly Detection in Medical Images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 189–198. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_18
Rohlfing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapping 31(5), 798–819 (2010)
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318–14328 (2022)
Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 14902–14912 (2021)
Schell, M., et al.: Automated brain extraction of multi-sequence MRI using artificial neural networks. European Congress of Radiology-ECR 2019 (2019)
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)
Silva-Rodríguez, J., Naranjo, V., Dolz, J.: Constrained unsupervised anomaly segmentation. arXiv preprint arXiv:2203.01671 (2022)
Simarro Viana, J., de la Rosa, E., Vande Vyvere, T., Robben, D., Sima, D.M., et al.: Unsupervised 3d brain anomaly detection. In: International MICCAI Brainlesion Workshop, pp. 133–142. Springer (2020)
Tan, J., Hou, B., Batten, J., Qiu, H., Kainz, B.: Detecting outliers with foreign patch interpolation. arXiv preprint arXiv:2011.04197 (2020)
Wyatt, J., Leach, A., Schmon, S.M., Willcocks, C.G.: Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 650–656 (2022)
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, pp. 540–556. PMLR (2019)
Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
Zhou, Z., Sodha, V., Pang, J., Gotway, M.B., Liang, J.: Models genesis. Med. Image Anal. 67, 101840 (2021)
Zimmerer, D., et al.: Mood 2020: a public benchmark for out-of-distribution detection and localization on medical images. IEEE Trans. Med. Imaging 41(10), 2728–2738 (2022)
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., et al.: Medical out-of-distribution analysis challenge (2022). https://doi.org/10.5281/zenodo.6362313
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Frolova, D., Katrutsa, A., Oseledets, I. (2023). Feature-Based Pipeline for Improving Unsupervised Anomaly Segmentation on Medical Images. In: Sudre, C.H., Baumgartner, C.F., Dalca, A., Mehta, R., Qin, C., Wells, W.M. (eds) Uncertainty for Safe Utilization of Machine Learning in Medical Imaging. UNSURE 2023. Lecture Notes in Computer Science, vol 14291. Springer, Cham. https://doi.org/10.1007/978-3-031-44336-7_12
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