Raman spectroscopic histology using machine learning for nonalcoholic fatty liver disease.
Raman hyperspectral imaging
machine learning
nonalcoholic fatty liver disease
rate-distortion theory
superpixel segmentation
Journal
FEBS letters
ISSN: 1873-3468
Titre abrégé: FEBS Lett
Pays: England
ID NLM: 0155157
Informations de publication
Date de publication:
09 2019
09 2019
Historique:
received:
24
04
2019
revised:
03
06
2019
accepted:
27
06
2019
pubmed:
30
6
2019
medline:
23
6
2020
entrez:
30
6
2019
Statut:
ppublish
Résumé
Histopathology requires the expertise of specialists to diagnose morphological features of cells and tissues. Raman imaging can provide additional biochemical information to benefit histological disease diagnosis. Using a dietary model of nonalcoholic fatty liver disease in rats, we combine Raman imaging with machine learning and information theory to evaluate cellular-level information in liver tissue samples. After increasing signal-to-noise ratio in the Raman images through superpixel segmentation, we extract biochemically distinct regions within liver tissues, allowing for quantification of characteristic biochemical components such as vitamin A and lipids. Armed with microscopic information about the biochemical composition of the liver tissues, we group tissues having similar composition, providing a descriptor enabling inference of tissue states, contributing valuable information to histological inspection.
Identifiants
pubmed: 31254349
doi: 10.1002/1873-3468.13520
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2535-2544Subventions
Organisme : Japan Society for the Promotion of Science
ID : 25287105
Pays : International
Organisme : Japan Society for the Promotion of Science
ID : 2560044
Pays : International
Organisme : Core Research for Evolutional Science and Technology
ID : JPMJCR1662
Pays : International
Organisme : Research Program of 'Five-star Alliance' in 'NJRC Mater. & Dev.'
Pays : International
Informations de copyright
© 2019 Federation of European Biochemical Societies.
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