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Patch-Based Abnormality Maps for Improved Deep Learning-Based Classification of Huntington’s Disease

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Abstract

Deep learning techniques have demonstrated state-of-the-art performances in many medical imaging applications. These methods can efficiently learn specific patterns. An alternative approach to deep learning is patch-based grading methods, which aim to detect local similarities and differences between groups of subjects. This latter approach usually requires less training data compared to deep learning techniques. In this work, we propose two major contributions: first, we combine patch-based and deep learning methods. Second, we propose to extend the patch-based grading method to a new patch-based abnormality metric. Our method enables us to detect localized structural abnormalities in a test image by comparison to a template library consisting of images from a variety of healthy controls. We evaluate our method by comparing classification performance using different sets of features and models. Our experiments show that our novel patch-based abnormality metric increases deep learning performance from 91.3% to 95.8% of accuracy compared to standard deep learning approaches based on the MRI intensity.

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References

  1. Arbabshirani, M.R., Plis, S., Sui, J., Calhoun, V.D.: Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. NeuroImage145, 137–165 (2017)

    Article  Google Scholar 

  2. Coupé, P., et al.: Scoring by nonlocal image patch estimator for early detection of Alzheimer’s disease. NeuroImage: Clin.1(1), 141–152 (2012)

    Google Scholar 

  3. Coupé, P., Deledalle, C.-A., Dossal, C., Allard, M.: Sparse-based morphometry: principle and application to Alzheimer’s disease. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B.C., Rueckert, D. (eds.) Patch-MI 2016. LNCS, vol. 9993, pp. 43–50. Springer, Cham (2016).https://doi.org/10.1007/978-3-319-47118-1_6

    Chapter  Google Scholar 

  4. Dayalu, P., Albin, R.L.: Huntington disease: pathogenesis and treatment. Neurol. Clin.33(1), 101–114 (2015)

    Article  Google Scholar 

  5. Giraud, R., Ta, V.T., Papadakis, N., Manjón, J.V., Collins, D.L., Coupé, P., Alzheimer’s Disease Neuroimaging Initiative et al.: An optimized patchmatch for multi-scale and multi-feature label fusion. NeuroImage124, 770–782 (2016)

    Google Scholar 

  6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  7. Hett, K., Ta, V.T., Catheline, G., Tourdias, T., Manjón, J.V., Coupe, P.: Multimodal hippocampal subfield grading for Alzheimer’s disease classification. Sci. Rep.9(1), 1–16 (2019)

    Article  Google Scholar 

  8. Hett, K., Ta, V.T., Manjón, J.V., Coupé, P., Alzheimer’s Disease Neuroimaging Initiative et al.: Adaptive fusion of texture-based grading for Alzheimer’s disease classification. Computerized Medical Imaging and Graphics70, 8–16 (2018)

    Google Scholar 

  9. Hett, K., Johnson, H., Coupé, P., Paulsen, J.S., Long, J.D., Oguz, I.: Tensor-based grading: a novel patch-based grading approach for the analysis of deformation fields in Huntington’s disease. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1091–1095. IEEE (2020)

    Google Scholar 

  10. Kim, E.Y., Lourens, S., Long, J.D., Paulsen, J.S., Johnson, H.J.: Preliminary analysis using multi-atlas labeling algorithms for tracing longitudinal change. Front. Neurosci.9, 242 (2015)

    Article  Google Scholar 

  11. Parisot, S., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med. Image Anal.48, 117–130 (2018)

    Article  Google Scholar 

  12. Paulsen, J.S., et al.: Detection of Huntington’s disease decades before diagnosis: the Predict-HD study. J. Neurol. Neurosurg. Psychiatry79(8), 874–880 (2008)

    Article  Google Scholar 

  13. Paulsen, J.S., et al.: Clinical and biomarker changes in premanifest Huntington disease show trial feasibility: a decade of the PREDICT-HD study. Front. Aging Neurosci.6, 78 (2014)

    Article  Google Scholar 

  14. Pierson, R., et al.: Fully automated analysis using BRAINS: AutoWorkup. NeuroImage54(1), 328–336 (2011)

    Article  Google Scholar 

  15. Ross, C.A., et al.: Huntington disease: natural history, biomarkers and prospects for therapeutics. Nat. Rev. Neurol.10(4), 204 (2014)

    Article  Google Scholar 

  16. Suk, H.I., Lee, S.W., Shen, D., Alzheimer’s Disease Neuroimaging Initiative et al.: Deep ensemble learning of sparse regression models for brain disease diagnosis. Medical image analysis37, 101–113 (2017)

    Google Scholar 

  17. Tong, T., Gao, Q., Guerrero, R., Ledig, C., Chen, L., Rueckert, D., Alzheimer’s Disease Neuroimaging Initiative et al.: A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer’s disease. IEEE Trans. Biomed. Eng.64(1), 155–165 (2016)

    Google Scholar 

  18. Tong, T., et al.: Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting. NeuroImage: Clin.15, 613–624 (2017)

    Google Scholar 

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Acknowledgements

This work was supported, in part, by the NIH grants R01–NS094456 and U01–NS106845. The PREDICT-HD study was funded by the NCATS, the NIH (NIH; R01–NS040068, U01–NS105509, U01–NS103475), and CHDI.org. Vanderbilt University Institutional Review Board has approved this study.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA

    Kilian Hett & Ipek Oguz

  2. Bordeaux INP, University of Bordeaux, CNRS, IMS, UMR 5218, Talence, France

    Rémi Giraud

  3. Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA

    Hans Johnson

  4. Department of Neurology, University of Wisconsin, Madison, WI, USA

    Jane S. Paulsen

  5. Department of Psychiatry, University of Iowa, Iowa City, IA, USA

    Jeffrey D. Long

  6. Department of Biostatistics, University of Iowa, Iowa City, IA, USA

    Jeffrey D. Long

Authors
  1. Kilian Hett

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  2. Rémi Giraud

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  3. Hans Johnson

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  4. Jane S. Paulsen

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  5. Jeffrey D. Long

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  6. Ipek Oguz

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Corresponding author

Correspondence toKilian Hett.

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Anne L. Martel

  2. The University of British Columbia, Vancouver, BC, Canada

    Purang Abolmaesumi

  3. University College London, London, UK

    Danail Stoyanov

  4. École Centrale de Nantes, Nantes, France

    Diana Mateus

  5. EURECOM, Biot, France

    Maria A. Zuluaga

  6. Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  7. Sorbonne University, Paris, France

    Daniel Racoceanu

  8. The Hebrew University of Jerusalem, Jerusalem, Israel

    Leo Joskowicz

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© 2020 Springer Nature Switzerland AG

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Hett, K., Giraud, R., Johnson, H., Paulsen, J.S., Long, J.D., Oguz, I. (2020). Patch-Based Abnormality Maps for Improved Deep Learning-Based Classification of Huntington’s Disease. In: Martel, A.L.,et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_62

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