MRI atlas of the pituitary gland in young female adults.
Atlas
Automatic segmentation
Human brain
MRI
Pituitary gland
Journal
Brain structure & function
ISSN: 1863-2661
Titre abrégé: Brain Struct Funct
Pays: Germany
ID NLM: 101282001
Informations de publication
Date de publication:
19 Mar 2024
19 Mar 2024
Historique:
received:
01
12
2023
accepted:
20
02
2024
medline:
19
3
2024
pubmed:
19
3
2024
entrez:
19
3
2024
Statut:
aheadofprint
Résumé
The probabilistic topography and inter-individual variability of the pituitary gland (PG) remain undetermined. The absence of a standardized reference atlas hinders research on PG volumetrics. In this study, we aimed at creating maximum probability maps for the anterior and posterior PG in young female adults. We manually delineated the anterior and posterior parts of the pituitary glands in 26 healthy subjects using high-resolution MRI T1 images. A three-step procedure and a cost function-masking approach were employed to optimize spatial normalization for the PG. We generated probabilistic atlases and maximum probability maps, which were subsequently coregistered back to the subjects' space and compared to manual delineations. Manual measurements led to a total pituitary volume of 705 ± 88 mm³, with the anterior and posterior volumes measuring 614 ± 82 mm³ and 91 ± 20 mm³, respectively. The mean relative volume difference between manual and atlas-based estimations was 1.3%. The global pituitary atlas exhibited an 80% (± 9%) overlap for the DICE index and 67% (± 11%) for the Jaccard index. Similarly, these values were 77% (± 13%) and 64% (± 14%) for the anterior pituitary atlas and 62% (± 21%) and 47% (± 17%) for the posterior PG atlas, respectively. We observed a substantial concordance and a significant correlation between the volume estimations of the manual and atlas-based methods for the global pituitary and anterior volumes. The maximum probability maps of the anterior and posterior PG lay the groundwork for automatic atlas-based segmentation methods and the standardized analysis of large PG datasets.
Identifiants
pubmed: 38502330
doi: 10.1007/s00429-024-02779-3
pii: 10.1007/s00429-024-02779-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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