Spatial quantification of clinical biomarker pharmacokinetics through deep learning-based segmentation and signal-oriented analysis of MSOT data.
AUC, Area under the curve
Biomarkers
DAG, Directed acyclic graph
DL, Deep learning
Deep learning
GUI, Graphical user interface
ICG, Indocyanine green
ImageJ plugin
MSE, Mean squared error
MSOT, Multispectral optoacoustic tomography
Mcat, MSOT cluster analysis toolkit
Multispectral optoacoustic tomography
PCI, Peritoneal contamination and infection
Pharmacokinetics
Quantitative image analysis
ROI, Region of interest
Sepsis
WAC, Weighted-average curve
Journal
Photoacoustics
ISSN: 2213-5979
Titre abrégé: Photoacoustics
Pays: Germany
ID NLM: 101622604
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
received:
13
02
2022
revised:
07
04
2022
accepted:
22
04
2022
entrez:
11
5
2022
pubmed:
12
5
2022
medline:
12
5
2022
Statut:
epublish
Résumé
Although multispectral optoacoustic tomography (MSOT) significantly evolved over the last several years, there is a lack of quantitative methods for analysing this type of image data. Current analytical methods characterise the MSOT signal in manually defined regions of interest outlining selected tissue areas. These methods demand expert knowledge of the sample anatomy, are time consuming, highly subjective and prone to user bias. Here we present our fully automated open-source MSOT cluster analysis toolkit
Identifiants
pubmed: 35541023
doi: 10.1016/j.pacs.2022.100361
pii: S2213-5979(22)00029-5
pmc: PMC9079355
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100361Informations de copyright
© 2022 The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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