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.
Keywords : acute ischemic stroke; optical diffraction
tomography; thrombus composition; label-free; deep learning
Abbreviations: ODT , optical diffraction tomography;DL, deep learning; RBC , red blood cells; BF ,
bright-field; RI , refractive index; DMD , digital
micromirror device; FOV , field of view; CatSIM ,
categorical image similarity metric; ROSE , rapid on-site
evaluation
1 INTRODUCTION
Acute ischemic stroke (AIS) is a fatal disease that requires immediate
and appropriate treatment to prevent death or devastating sequelae. The
composition of the thrombus, or blood clot, provides useful evidence in
determining the appropriate treatment for AIS. The histological
composition and structure of AIS thrombi offer insights into disease
pathophysiology, such as pre-interventional migration, vascular origin,
and thrombus age, and may be used to decide the best treatment options
for patients. The thrombus composition is also related to the clinical
outcomes of recanalization with thrombolysis or endovascular
thrombectomy.[1] Studies have identified the
pre-interventional migration, vascular origin, and age of thrombi from
their histological composition and structure,[2,3]while others have differentiated cardioembolic from non-cardioembolic
origin using the proportion of fibrin/platelets or red blood cells
(RBCs) in thrombi.[4,5]
Conventional approaches to evaluating the histological composition of a
thrombus require pathologists to manually screen stained thrombus
sections under a bright-field (BF) microscope. Through microscopic
examination, pathologists differentiate the type of thrombi into
RBC-dominant or fibrin/platelet-dominant and report distinct features
based on the stained thrombus slide.[6-8] However,
color-based analysis of a thrombus slide is highly influenced by the
staining quality and may lead to generalization problems due to staining
variability.[9] Additionally, the fixation and
staining procedures are labor-intensive and time-consuming, limiting
efficiency and throughput.
In this study, we propose and experimentally demonstrate a label-free
histological quantification framework to rapidly assess AIS thrombi
using optical diffraction tomography (ODT) and a deep learning (DL)
algorithm (Figure 1 ). We leverage the label-free structural
assessment of ODT with the statistical image recognition of deep
learning to automatically characterize a whole unstained slide of the
thrombus. ODT is a label-free 3D imaging method for cells and tissues
that returns the refractive index (RI) distribution of the
sample.[10,11] The label-free nature of ODT
simplifies sample preparation and enables efficient imaging, while also
providing the ability to extract various quantitative biophysical
properties from the RI distribution.