期刊
PHOTOACOUSTICS
卷 26, 期 -, 页码 -出版社
ELSEVIER GMBH
DOI: 10.1016/j.pacs.2022.100361
关键词
Multispectral optoacoustic tomography; Quantitative image analysis; Deep learning; ImageJ plugin; Biomarkers; Pharmacokinetics; Sepsis
类别
资金
- German Research Foundation (DFG) , Germany
- Leibniz ScienceCampus InfectoOptics Jena, Germany
- Leibniz Association
- Federal Ministry of Education and Research (BMBF) , Germany [01EO1502]
- International Leibniz Research School for Microbial and Biomolecular Interactions Jena (ILRS Jena) , Germany
- Interdisciplinary Centre for Clinical Research, Germany [AMSP05]
- [316213987]
This article introduces a fully automated open-source MSOT cluster analysis toolkit that provides a quantitative method for analyzing image data and an objective and automated approach to extract the biodistribution of biomarkers.
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 Mcat that was designed to overcome these shortcomings. It employs a deep learning-based approach for initial image segmentation followed by unsupervised machine learning to identify regions of similar signal kinetics. It provides an objective and automated approach to quantify the pharmacokinetics and extract the biodistribution of biomarkers from MSOT data. We exemplify our generally applicable analysis method by quantifying liver function in a preclinical sepsis model whilst highlighting the advantages of our new approach compared to the severe limitations of existing analysis procedures.
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