Label-free histological analysis of retrieved thrombi in acute ischemic stroke using optical diffraction tomography and deep learning.
J Biophotonics
; 16(8): e202300067, 2023 08.
Article
en En
| MEDLINE
| ID: mdl-37170722
ABSTRACT
For patients with acute ischemic stroke, histological quantification of thrombus composition provides evidence for determining appropriate treatment. However, the traditional manual segmentation of stained thrombi is laborious and inconsistent. In this study, we propose a label-free method that combines optical diffraction tomography (ODT) and deep learning (DL) to automate the histological quantification process. The DL model classifies ODT image patches with 95% accuracy, and the collective prediction generates a whole-slide map of red blood cells and fibrin. The resulting whole-slide composition displays an average error of 1.1% and does not experience staining variability, facilitating faster analysis with reduced labor. The present approach will enable rapid and quantitative evaluation of blood clot composition, expediting the preclinical research and diagnosis of cardiovascular diseases.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Trombosis
/
Isquemia Encefálica
/
Accidente Cerebrovascular
/
Aprendizaje Profundo
/
Accidente Cerebrovascular Isquémico
Tipo de estudio:
Guideline
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
J Biophotonics
Asunto de la revista:
BIOFISICA
Año:
2023
Tipo del documento:
Article