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We trained on the healthy aging cohort and did not carry out any adaption or fine-tuning prior to the application to Parkinson\u2019s disease subjects.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>\n                           <jats:bold>Results<\/jats:bold>\n                        <\/jats:title>\n                <jats:p>The localization and segmentation pipeline demonstrated sufficient performance as measured by Euclidean distance (on average around 1.3mm on healthy aging subjects and 2.2mm in Parkinson\u2019s disease subjects) and Dice similarity coefficient (overall around <jats:inline-formula><jats:alternatives><jats:tex-math>$$71\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mn>71<\/mml:mn>\n                      <mml:mo>%<\/mml:mo>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> on healthy aging subjects and <jats:inline-formula><jats:alternatives><jats:tex-math>$$60\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mn>60<\/mml:mn>\n                      <mml:mo>%<\/mml:mo>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> for subjects with Parkinson\u2019s disease) as well as promising agreement with respect to contrast ratios in terms of intraclass correlation coefficient of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\ge 0.80$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mo>\u2265<\/mml:mo>\n                      <mml:mn>0.80<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> for healthy aging subjects compared to a manual segmentation procedure. Lower values (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\ge 0.48$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mo>\u2265<\/mml:mo>\n                      <mml:mn>0.48<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) for Parkinson\u2019s disease subjects indicate the need for further investigation and tests before the application to clinical samples.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>\n                           <jats:bold>Conclusion<\/jats:bold>\n                        <\/jats:title>\n                <jats:p>These promising results suggest the usability of the proposed algorithm for data of healthy aging subjects and pave the way for further investigations using this approach on different clinical datasets to validate its practical usability more conclusively.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-021-02528-5","type":"journal-article","created":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T14:02:31Z","timestamp":1637330551000},"page":"2129-2135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Fully automated deep learning-based localization and segmentation of the locus coeruleus in aging and Parkinson\u2019s disease using neuromelanin-sensitive MRI"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3838-3345","authenticated-orcid":false,"given":"Max","family":"D\u00fcnnwald","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Ernst","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emrah","family":"D\u00fczel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"T\u00f6nnies","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew J.","family":"Betts","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"Oeltze-Jafra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,19]]},"reference":[{"issue":"3","key":"2528_CR1","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1109\/TMI.2018.2872852","volume":"38","author":"M Ariz","year":"2019","unstructured":"Ariz M, Abad RC, Castellanos G, Mart\u00ednez M, Mu\u00f1oz-Barrutia A, Fern\u00e1ndez-Seara MA, Pastor P, Pastor MA, Ortiz-de Sol\u00f3rzano C (2019) Dynamic atlas-based segmentation and quantification of neuromelanin-rich brainstem structures in parkinson disease. 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Publication is planned in the foreseeable future.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"Written informed consent was obtained from all participants in accordance with the Declaration of Helsinki.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate\/Consent for publication"}},{"value":"The study received ethical approval from the ethics commission of the Faculty of Medicine at the University of Magdeburg.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}