{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T07:13:41Z","timestamp":1789456421661,"version":"build-2803163510"},"reference-count":0,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The semi-quantitative assessment of brain [18F]FDG PET provides a more objective interpretation and improved accuracy in differentiating across neurodegenerative diseases. However, the correct identification of anatomical regions of interest without a structural MRI is challenging. Thus, this study aims to develop a deep-learning-based (DL-based) model for the automatic segmentation of 52 anatomical regions in brain [18F]FDG PET images and validate it across different metabolic profiles.<\/jats:p>\n                  <jats:p>1628 brain [18F]FDG PET images of 1099 subjects were included in the internal dataset, comprising cognitively normal subjects (n=537), and patients with mild cognitive impairment (n=538), subjective memory concerns (n=70), Alzheimer\u2019s disease (n=330), frontotemporal lobar degeneration (n=95) and Lewy body dementia (n=58). Train-test split yielded 1109\/519 images (631\/468 subjects) for training and internal testing of a DL-based model, respectively. An additional dataset of 108 [18F]FDG PET images was included for external validation. Ground-truth segmentation was performed on the paired T1-weighted MRI image for each [18F]FDG PET image, for a total of 52 anatomical regions of interest. Atlas-based segmentation was used as a benchmark. The Dice similarity coefficient (DSC) was used to assess segmentation performance. Agreement in mean pons-based standardised uptake value ratio (SUVRmean) quantification was assessed through the intraclass correlation coefficient (ICC) and relative deviation in absolute value.<\/jats:p>\n                  <jats:p>Per-region mean DSC for the DL-based segmentations ranged from 0.729 to 0.923 in the internal test set. Global mean DSC was 0.84\u00b10.06. SUVRmean quantification of the different regions using the DL-based segmentation masks showed strong agreement with that obtained using the ground-truth segmentation masks, with an average ICC of 0.96\u00b10.02. The per-region average of relative SUVRmean deviation did not exceed 5%. DL-based segmentation significantly outperformed atlas-based segmentation (p&amp;lt;0.05). Similar segmentation performance was obtained in the external validation dataset.<\/jats:p>\n                  <jats:p>DL-based anatomical segmentation of brain [18F]FDG PET proved to be robust across a wide spectrum of neurodegenerative diseases. Semi-quantitative assessment was comparable with that obtained with MRI-based segmentation. DL-based segmentation, therefore, proved to be a reliable alternative when MRI isn\u2019t available, and is a better option than the commonly used atlas-based approach.<\/jats:p>","DOI":"10.1093\/brain\/awag314","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T11:52:36Z","timestamp":1789127556000},"source":"Crossref","is-referenced-by-count":0,"title":["Brain fluorodeoxyglucose PET anatomical segmentation via AI: extensive validation in the neurodegenerative spectrum"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1990-8759","authenticated-orcid":false,"given":"Lu\u00edsa C","family":"Silva","sequence":"first","affiliation":[{"name":"Champalimaud Clinical Centre, Champalimaud Foundation , 1400-038 Lisbon ,","place":["Portugal"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9468-8894","authenticated-orcid":false,"given":"Francisco P M","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Champalimaud Clinical Centre, Champalimaud Foundation , 1400-038 Lisbon ,","place":["Portugal"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8039-4924","authenticated-orcid":false,"given":"Durval C","family":"Costa","sequence":"additional","affiliation":[{"name":"Champalimaud Clinical Centre, Champalimaud Foundation , 1400-038 Lisbon ,","place":["Portugal"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,15]]},"container-title":["Brain"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/brain\/advance-article-pdf\/doi\/10.1093\/brain\/awag314\/71082894\/awag314.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/brain\/advance-article-pdf\/doi\/10.1093\/brain\/awag314\/71082894\/awag314.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T06:54:08Z","timestamp":1789455248000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/brain\/advance-article\/doi\/10.1093\/brain\/awag314\/8795202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/brain\/awag314","relation":{},"ISSN":["0006-8950","1460-2156"],"issn-type":[{"value":"0006-8950","type":"print"},{"value":"1460-2156","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]},"article-number":"awag314"}}