Abstract
Parkinson’s disease (PD) is a neurodegenerative disease. PD patients may have serious movement disorders and mental problems. The current diagnosis requires a professionally trained medical doctor to take a long period for it. Different doctors may even have different accuracies. Recently advances in deep learning-based medical image classification make it is possible to diagnose PD automatically. Different from most of the existing works on magnetic resonance images, we use diffusion tensor imaging (DTI) in that it can reflect functional data of the brain. We propose a sub-models integration framework based on convolutional neural networks (CNNs) for Parkinson’s disease. Each sub-region of the brain is used to train a unique CNN model, named sub-model, and the selective stacking algorithm is used to screen these sub-models. It obtains the classification accuracy of 92.4% on the cross-validation dataset. In addition, it can provide that which sub-regions play a role in the judgment of the final result so that this framework has stronger practical application than an end-to-end model.
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62020106011 and Grant U19A2052 and in part by the Sichuan Science and Technology Program under Grant 2019YFH008.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acosta-Cabronero, J., Alley, S., Williams, G.B., Pengas, G., Nestor, P.J.: Diffusion tensor metrics as biomarkers in Alzheimer’s disease. PLoS ONE 7(11), e49072 (2012)
Adler, C.H., et al.: Low clinical diagnostic accuracy of early vs advanced Parkinson disease: clinicopathologic study. Neurology 83(5), 406–412 (2014)
Carlesimo, G., Piras, F., Assogna, F., Pontieri, F., Caltagirone, C., Spalletta, G.: Hippocampal abnormalities and memory deficits in Parkinson disease: a multimodal imaging study. Neurology 78(24), 1939–1945 (2012)
Chang, C.C., et al.: Clinical significance of the pallidoreticular pathway in patients with carbon monoxide intoxication. Brain 134(12), 3632–3646 (2011)
Choi, S.H., Jung, T.M., Lee, J.E., Lee, S.K., Sohn, Y.H., Lee, P.H.: Volumetric analysis of the substantia innominata in patients with Parkinson’s disease according to cognitive status. Neurobiol. Aging 33(7), 1265–1272 (2012)
Cochrane, C.J., Ebmeier, K.P.: Diffusion tensor imaging in parkinsonian syndromes: a systematic review and meta-analysis. Neurology 80(9), 857–864 (2013)
Frid, A., et al.: Computational diagnosis of Parkinson’s disease directly from natural speech using machine learning techniques. In: 2014 IEEE International Conference on Software Science, Technology and Engineering, pp. 50–53. IEEE (2014)
Gallicchio, C., Micheli, A., Pedrelli, L.: Deep echo state networks for diagnosis of Parkinson’s disease. arXiv preprint arXiv:1802.06708 (2018)
Gattellaro, G., et al.: White matter involvement in idiopathic Parkinson disease: a diffusion tensor imaging study. Am. J. Neuroradiol. 30(6), 1222–1226 (2009)
Hirschauer, T.J., Adeli, H., Buford, J.A.: Computer-aided diagnosis of Parkinson’s disease using enhanced probabilistic neural network. J. Med. Syst. 39(11), 1–12 (2015)
Kendi, A.K., Lehericy, S., Luciana, M., Ugurbil, K., Tuite, P.: Altered diffusion in the frontal lobe in Parkinson disease. Am. J. Neuroradiol. 29(3), 501–505 (2008)
Kikuchi, A., et al.: Hypometabolism in the supplementary and anterior cingulate cortices is related to dysphagia in Parkinson’s disease: a cross-sectional and 3-year longitudinal cohort study. BMJ Open 3(3), e002249 (2013)
Lu, C.S., et al.: Alterations of diffusion tensor MRI parameters in the brains of patients with Parkinson’s disease compared with normal brains: possible diagnostic use. Eur. Radiol. 26(11), 3978–3988 (2016)
Marek, K., et al.: The Parkinson progression marker initiative (PPMI). Prog. Neurobiol. 95(4), 629–635 (2011)
Menke, R.A., et al.: MRI characteristics of the substantia nigra in Parkinson’s disease: a combined quantitative T1 and DTI study. Neuroimage 47(2), 435–441 (2009)
Pereira, C.R., et al.: A step towards the automated diagnosis of Parkinson’s disease: analyzing handwriting movements. In: 2015 IEEE 28th International Symposium on Computer-Based Medical Systems, pp. 171–176. IEEE (2015)
Pereira, C.R., Weber, S.A., Hook, C., Rosa, G.H., Papa, J.P.: Deep learning-aided Parkinson’s disease diagnosis from handwritten dynamics. In: 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 340–346. IEEE (2016)
Sakar, B.E., et al.: Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J. Biomed. Health Inform. 17(4), 828–834 (2013)
Sun, W., Zheng, B., Qian, W.: Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput. Biol. Med. 89, 530–539 (2017)
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Tessitore, A., et al.: Regional gray matter atrophy in patients with Parkinson disease and freezing of gait. Am. J. Neuroradiol. 33(9), 1804–1809 (2012)
Vaillancourt, D., et al.: High-resolution diffusion tensor imaging in the substantia nigra of de novo Parkinson disease. Neurology 72(16), 1378–1384 (2009)
Wang, J., et al.: Microstructural changes in patients with progressive supranuclear palsy: a diffusion tensor imaging study. J. Magn. Reson. Imaging 32(1), 69–75 (2010)
Zhang, Y., et al.: Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 68(1), 13–19 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, H., Tsai, CC., Zhu, C., Zhou, M., Wang, JJ., Liu, Y. (2021). Deep Learning-Based Regional Sub-models Integration for Parkinson’s Disease Diagnosis Using Diffusion Tensor Imaging. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_60
Download citation
DOI: https://doi.org/10.1007/978-3-030-87358-5_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87357-8
Online ISBN: 978-3-030-87358-5
eBook Packages: Computer ScienceComputer Science (R0)