Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images.
Image segmentation
Sarcopenia
Three-dimensional level set methods
X-ray Computed Tomography (CT)
Journal
BMC medical imaging
ISSN: 1471-2342
Titre abrégé: BMC Med Imaging
Pays: England
ID NLM: 100968553
Informations de publication
Date de publication:
19 Sep 2024
19 Sep 2024
Historique:
received:
06
01
2024
accepted:
06
09
2024
medline:
20
9
2024
pubmed:
20
9
2024
entrez:
19
9
2024
Statut:
epublish
Résumé
The analysis of the psoas muscle in morphological and functional imaging has proved to be an accurate approach to assess sarcopenia, i.e. a systemic loss of skeletal muscle mass and function that may be correlated to multifactorial etiological aspects. The inclusion of sarcopenia assessment into a radiological workflow would need the implementation of computational pipelines for image processing that guarantee segmentation reliability and a significant degree of automation. The present study utilizes three-dimensional numerical schemes for psoas segmentation in low-dose X-ray computed tomography images. Specifically, here we focused on the level set methodology and compared the performances of two standard approaches, a classical evolution model and a three-dimension geodesic model, with the performances of an original first-order modification of this latter one. The results of this analysis show that these gradient-based schemes guarantee reliability with respect to manual segmentation and that the first-order scheme requires a computational burden that is significantly smaller than the one needed by the second-order approach.
Identifiants
pubmed: 39300334
doi: 10.1186/s12880-024-01423-0
pii: 10.1186/s12880-024-01423-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
251Subventions
Organisme : PROGRAMMA OPERATIVO REGIONE LIGURIA FONDO SOCIALE EUROPEO 2014-2020
ID : RLOF18ASSRIC/58/1
Organisme : Hub Life Science - Digital Health (LSH-DH) PNC-E3-2022-23683267 - Progetto DHEAL-COM
ID : D33C22001980001
Organisme : Hub Life Science - Digital Health (LSH-DH) PNC-E3-2022-23683267 - Progetto DHEAL-COM
ID : D33C22001980001
Informations de copyright
© 2024. The Author(s).
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