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
Arbabshirani, M.R., Plis, S., Sui, J., Calhoun, V.D.: Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. NeuroImage 145, 137–165 (2017)
Coupé, P., et al.: Scoring by nonlocal image patch estimator for early detection of Alzheimer’s disease. NeuroImage: Clin. 1(1), 141–152 (2012)
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
Dayalu, P., Albin, R.L.: Huntington disease: pathogenesis and treatment. Neurol. Clin. 33(1), 101–114 (2015)
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. NeuroImage 124, 770–782 (2016)
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)
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)
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 Graphics 70, 8–16 (2018)
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)
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)
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)
Paulsen, J.S., et al.: Detection of Huntington’s disease decades before diagnosis: the Predict-HD study. J. Neurol. Neurosurg. Psychiatry 79(8), 874–880 (2008)
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)
Pierson, R., et al.: Fully automated analysis using BRAINS: AutoWorkup. NeuroImage 54(1), 328–336 (2011)
Ross, C.A., et al.: Huntington disease: natural history, biomarkers and prospects for therapeutics. Nat. Rev. Neurol. 10(4), 204 (2014)
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 analysis 37, 101–113 (2017)
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)
Tong, T., et al.: Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting. NeuroImage: Clin. 15, 613–624 (2017)
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.
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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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DOI: https://doi.org/10.1007/978-3-030-59728-3_62
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