Label-free histological analysis of retrieved thrombi in acute ischemic
stroke using optical diffraction tomography and deep learning
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.