James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, Abdelalim A, Abdollahpour I, Abdulkader RS, Abebe Z, Abera SF, Abil OZ, Abraha HN, Abu-Raddad LJ, Abu-Rmeileh NME, Accrombessi MMK, Acharya D et al (2018) Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. The Lancet 392(10159):1789–1858
Berre AL, Kamagata K, Otsuka Y, Andica C, Hatano T, Saccenti L, Ogawa T, Takeshige-Amano H, Wada A, Suzuki M, Hagiwara A, Irie R, Hori M, Oyama G, Shimo Y, Umemura A, Hattori N, Aoki S (2019) Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI. Neuroradiology 61:1387–1395
Article PubMed PubMed Central Google Scholar
He N, Chen Y, LeWitt PA, Yan F, Haacke EM (2023) Application of neuromelanin MR imaging in Parkinson disease. J Magn Resonance Imaging 57:337–352
Sasaki M, Shibata E, Tohyama K (2006) Neuromelanin magnetic resonance imaging of locus ceruleus and substantia nigra in Parkinson’s disease. Neuro Rep 17(11):1215–1218
Kashihara K, Shinya T, Higaki F (2011) Neuromelanin magnetic resonance imaging of nigral volume loss in patients with Parkinson’s disease. J Clin Neurosci 18(8):1093–1096
Drui G, Carnicella S, Carcenac C, Favier M, Bertrand A, Boulet S, Savasta M (2014) Loss of dopaminergic nigrostriatal neurons accounts for the motivational and affective deficits in Parkinsons disease. Mol Psychiatry 19:358–367
Article CAS PubMed Google Scholar
Hu T, Itoh H, Oda M, Hayashi Y, Lu Z, Saiki S, Hattori N, Kamagata K, Aoki S, Kumamaru KK, Akashi T, Mori K (2022) Enhancing model generalization for substantia nigra segmentation using a test-time normalization-based method. In: Proceedings of the 25th international conference on medical image computing and computer assisted intervention, LNCS 13437: 736–744
Hu T, Itoh H, Oda M, Saiki S, Hattori N, Kamagata K, Aoki S, Mori K (2023) Priority attention network with Bayesian learning for fully automatic segmentation of substantia Nigra from neuromelanin MRI. In: Proceedings of the SPIE medical imaging 2023: image processing SPIE 12464: 124643G
Itoh H, Hu T, Oda M, Saiki S, Kamagata K, Hattori N, Aoki S, Mori K (2022) Pattern analysis of substantia Nigra in Parkinson disease by fifth-order tensor decomposition and multi-sequence MRI. In: Proceedings of the 3rd international workshop on multiscale multimodal medical imaging LNCS 13594:63–75
Camacho M, Wilms M, Mouches P, Almgren H, Souza R, Camicioli R, Ismail Z, Monchi O, Forkert ND (2023) Explainable classification of Parkinson’s disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets,. Neuro Image Clin 38:103405
Shinde S, Prasad S, Saboo Y, Kaushick R, Saini J, Pal PK, Ingalhalikar M (2019) Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. NeuroImage: Clin 22:101748
Itoh H, Imiya A, Sakai T (2016) Pattern recognition in multilinear space and its applications: mathematics, computational algorithms and numerical validations. Mach Vis Appl 27:1259–1273
Smilde A, Bro R, Geladi P (2008) Multi-way analysis: applications in the chemical sciences, 1st edn. Wiley, New York
Kroonenberg PM (2008) Applied multiway data analysis, 1st edn. Wiley-Interscience, New York
Cichocki A, Zdunek R, Phan AH, Amari S (2009) Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. Wiley, New York
Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500
Carroll J, Chang J-J (1970) Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition. Psychometrika 35(3):283–319
Harshman RA (1970) Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis. UCLA Working Papers in Phonetics 16:1–84
Shashua A, Hazan T (2005) Non-negative tensor factorization with applications to statistics and computer vision. In: Proceedings of the international conference on machine learning, pp 792–799
Fukunaga K, Koontz WLG (1970) Application of the Karhunen-Loéve expansion to feature selection and ordering. IEEE Trans Comput c–19(4):311–318
Duda RO, Hart PE, Stork DH (2000) Pattern classification, 2nd edn. Wiley Interscience, New York
Hughes AJ, Susan ED, Linda K, Andrew JL (1992) Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg 55(3):181–184
Jenkinson M, Bannister P, Brady JM, Smith SM (2002) Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2):825–841. https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT
Chris R, Matthew B (2001) Stereotaxic display of brain lesions. Behav Neurol 12:192–200. https://www.nitrc.org/projects/mricron/
Vapnik V, Lerner A (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24:774–780
Platt JC (1999) Probabilistic outputs for support vector machinesand comparisons to regularized likelihood methods. Adv Large Margin Classifier: 61-74
Ellison E, Chimelli L., Harding B, Love S, Lowe J, Roberts G, Vinters H (1998) Neuropathology. Mosby-Year Book
Rizzo G, Copetti M, Arcuti S, Martino D, Fontana A, Logroscino G (2016) Accuracy of clinical diagnosis of Parkinson disease. Neurology 86:566–576
Cho SJ, Bae YJ, Kim JM, Balik SH, Sunwoo L, Choi BS, Kim JH (2021) Diagnostic performance of neuromelanin-sensitive magnetic resonance imaging for patients with Parkinson’s disease and factor analysis for its heterogeneity: a systematic review and meta-analysis. Eur Radiol 31:1268–1280
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