{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T17:10:31Z","timestamp":1768410631659,"version":"3.49.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030873578","type":"print"},{"value":"9783030873585","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87358-5_60","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T23:54:11Z","timestamp":1632959651000},"page":"742-753","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-Based Regional Sub-models Integration for Parkinson\u2019s Disease Diagnosis Using Diffusion Tensor Imaging"],"prefix":"10.1007","author":[{"given":"Hengling","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Chih-Chien","family":"Tsai","sequence":"additional","affiliation":[]},{"given":"Ce","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Mingyi","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Jiun-Jie","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yipeng","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"60_CR1","doi-asserted-by":"crossref","unstructured":"Acosta-Cabronero, J., Alley, S., Williams, G.B., Pengas, G., Nestor, P.J.: Diffusion tensor metrics as biomarkers in Alzheimer\u2019s disease. PLoS ONE 7(11), e49072 (2012)","DOI":"10.1371\/journal.pone.0049072"},{"issue":"5","key":"60_CR2","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1212\/WNL.0000000000000641","volume":"83","author":"CH Adler","year":"2014","unstructured":"Adler, C.H., et al.: Low clinical diagnostic accuracy of early vs advanced Parkinson disease: clinicopathologic study. Neurology 83(5), 406\u2013412 (2014)","journal-title":"Neurology"},{"issue":"24","key":"60_CR3","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1212\/WNL.0b013e318259e1c5","volume":"78","author":"G Carlesimo","year":"2012","unstructured":"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\u20131945 (2012)","journal-title":"Neurology"},{"issue":"12","key":"60_CR4","doi-asserted-by":"publisher","first-page":"3632","DOI":"10.1093\/brain\/awr287","volume":"134","author":"CC Chang","year":"2011","unstructured":"Chang, C.C., et al.: Clinical significance of the pallidoreticular pathway in patients with carbon monoxide intoxication. Brain 134(12), 3632\u20133646 (2011)","journal-title":"Brain"},{"issue":"7","key":"60_CR5","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1016\/j.neurobiolaging.2010.11.015","volume":"33","author":"SH Choi","year":"2012","unstructured":"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\u2019s disease according to cognitive status. Neurobiol. Aging 33(7), 1265\u20131272 (2012)","journal-title":"Neurobiol. Aging"},{"issue":"9","key":"60_CR6","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1212\/WNL.0b013e318284070c","volume":"80","author":"CJ Cochrane","year":"2013","unstructured":"Cochrane, C.J., Ebmeier, K.P.: Diffusion tensor imaging in parkinsonian syndromes: a systematic review and meta-analysis. Neurology 80(9), 857\u2013864 (2013)","journal-title":"Neurology"},{"key":"60_CR7","doi-asserted-by":"crossref","unstructured":"Frid, A., et al.: Computational diagnosis of Parkinson\u2019s disease directly from natural speech using machine learning techniques. In: 2014 IEEE International Conference on Software Science, Technology and Engineering, pp. 50\u201353. IEEE (2014)","DOI":"10.1109\/SWSTE.2014.17"},{"key":"60_CR8","doi-asserted-by":"crossref","unstructured":"Gallicchio, C., Micheli, A., Pedrelli, L.: Deep echo state networks for diagnosis of Parkinson\u2019s disease. arXiv preprint arXiv:1802.06708 (2018)","DOI":"10.1109\/IJCNN.2018.8489464"},{"issue":"6","key":"60_CR9","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.3174\/ajnr.A1556","volume":"30","author":"G Gattellaro","year":"2009","unstructured":"Gattellaro, G., et al.: White matter involvement in idiopathic Parkinson disease: a diffusion tensor imaging study. Am. J. Neuroradiol. 30(6), 1222\u20131226 (2009)","journal-title":"Am. J. Neuroradiol."},{"issue":"11","key":"60_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-015-0353-9","volume":"39","author":"TJ Hirschauer","year":"2015","unstructured":"Hirschauer, T.J., Adeli, H., Buford, J.A.: Computer-aided diagnosis of Parkinson\u2019s disease using enhanced probabilistic neural network. J. Med. Syst. 39(11), 1\u201312 (2015)","journal-title":"J. Med. Syst."},{"issue":"3","key":"60_CR11","doi-asserted-by":"publisher","first-page":"501","DOI":"10.3174\/ajnr.A0850","volume":"29","author":"AK Kendi","year":"2008","unstructured":"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\u2013505 (2008)","journal-title":"Am. J. Neuroradiol."},{"key":"60_CR12","doi-asserted-by":"crossref","unstructured":"Kikuchi, A., et al.: Hypometabolism in the supplementary and anterior cingulate cortices is related to dysphagia in Parkinson\u2019s disease: a cross-sectional and 3-year longitudinal cohort study. BMJ Open 3(3), e002249 (2013)","DOI":"10.1136\/bmjopen-2012-002249"},{"issue":"11","key":"60_CR13","doi-asserted-by":"publisher","first-page":"3978","DOI":"10.1007\/s00330-016-4232-7","volume":"26","author":"CS Lu","year":"2016","unstructured":"Lu, C.S., et al.: Alterations of diffusion tensor MRI parameters in the brains of patients with Parkinson\u2019s disease compared with normal brains: possible diagnostic use. Eur. Radiol. 26(11), 3978\u20133988 (2016)","journal-title":"Eur. Radiol."},{"issue":"4","key":"60_CR14","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.pneurobio.2011.09.005","volume":"95","author":"K Marek","year":"2011","unstructured":"Marek, K., et al.: The Parkinson progression marker initiative (PPMI). Prog. Neurobiol. 95(4), 629\u2013635 (2011)","journal-title":"Prog. Neurobiol."},{"issue":"2","key":"60_CR15","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.neuroimage.2009.05.017","volume":"47","author":"RA Menke","year":"2009","unstructured":"Menke, R.A., et al.: MRI characteristics of the substantia nigra in Parkinson\u2019s disease: a combined quantitative T1 and DTI study. Neuroimage 47(2), 435\u2013441 (2009)","journal-title":"Neuroimage"},{"key":"60_CR16","doi-asserted-by":"crossref","unstructured":"Pereira, C.R., et al.: A step towards the automated diagnosis of Parkinson\u2019s disease: analyzing handwriting movements. In: 2015 IEEE 28th International Symposium on Computer-Based Medical Systems, pp. 171\u2013176. IEEE (2015)","DOI":"10.1109\/CBMS.2015.34"},{"key":"60_CR17","doi-asserted-by":"crossref","unstructured":"Pereira, C.R., Weber, S.A., Hook, C., Rosa, G.H., Papa, J.P.: Deep learning-aided Parkinson\u2019s disease diagnosis from handwritten dynamics. In: 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 340\u2013346. IEEE (2016)","DOI":"10.1109\/SIBGRAPI.2016.054"},{"issue":"4","key":"60_CR18","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/JBHI.2013.2245674","volume":"17","author":"BE Sakar","year":"2013","unstructured":"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\u2013834 (2013)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"60_CR19","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.compbiomed.2017.04.006","volume":"89","author":"W Sun","year":"2017","unstructured":"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\u2013539 (2017)","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"60_CR20","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299\u20131312 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"60_CR21","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.3174\/ajnr.A3066","volume":"33","author":"A Tessitore","year":"2012","unstructured":"Tessitore, A., et al.: Regional gray matter atrophy in patients with Parkinson disease and freezing of gait. Am. J. Neuroradiol. 33(9), 1804\u20131809 (2012)","journal-title":"Am. J. Neuroradiol."},{"issue":"16","key":"60_CR22","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1212\/01.wnl.0000340982.01727.6e","volume":"72","author":"D Vaillancourt","year":"2009","unstructured":"Vaillancourt, D., et al.: High-resolution diffusion tensor imaging in the substantia nigra of de novo Parkinson disease. Neurology 72(16), 1378\u20131384 (2009)","journal-title":"Neurology"},{"issue":"1","key":"60_CR23","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1002\/jmri.22229","volume":"32","author":"J Wang","year":"2010","unstructured":"Wang, J., et al.: Microstructural changes in patients with progressive supranuclear palsy: a diffusion tensor imaging study. J. Magn. Reson. Imaging 32(1), 69\u201375 (2010)","journal-title":"J. Magn. Reson. Imaging"},{"issue":"1","key":"60_CR24","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1212\/01.wnl.0000250326.77323.01","volume":"68","author":"Y Zhang","year":"2007","unstructured":"Zhang, Y., et al.: Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 68(1), 13\u201319 (2007)","journal-title":"Neurology"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87358-5_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T01:25:17Z","timestamp":1632965117000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87358-5_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030873578","9783030873585"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87358-5_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haikou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2021.csig.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"421","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"198","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"47% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was postponed due to the COVID19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}