4 DISCUSSION
This study presents a DL-based approach to classify the histological
composition of thrombi using label-free ODT images. The DL model
achieved over 95% accuracy in classifying patches into RBC, fibrin, and
background, enabling the prediction of thrombus composition at
whole-slide level. The use of ODT provided higher resolution and
contrast compared to conventional BF microscopy followed by H&E
staining. This label-free approach also allowed for rapid histological
analysis without staining and reduced the variability of
staining-dependent color distribution. The present work demonstrates the
rapid label-free detection a spatial distribution map of thrombus
composition that can be used to assess the thrombus response to
thrombectomy procedure.[39]
The results shown here can also be utilized for in-depth investigation
of a thrombus slide. The RI contrast and subcellular spatial resolution
provided by ODT facilitate a more detailed assessment of thrombus
structures compared to BF microscopy followed by H&E staining. Pores
within the fibrin network were more clearly observed in ODT than in BF
images. The border and texture of individual RBCs also appeared sharper
in ODT. Additionally, ODT distinguished structures that were not
differentiated by BF microscopy, such as regions of compressed RBCs
tentatively identified as polyhedrocytes. Polyhedrocytes may lead to
more thrombolysis-resistant thrombi by forming an impermeable layer that
blocks fibrinolytic enzymes from diffusing inside the thrombi. These
observations indicate that ODT measurement could potentially identify
indicators of thromboembolism.[40]
Additionally, accounting for volumetric information could provide a more
accurate determination of thrombus composition due to intra-thrombus
heterogeneity. Ground truth generation could also be improved by
utilizing immunofluorescence or immunohistochemistry. The DL design
could be modified to carry out segmentation instead of patch
classification for better reflecting the high-resolution geometry
provided by ODT. Using identical slides for ODT and bright-field imaging
may also improve pixel-level registration and provide more accurate
supervised learning for segmentation networks.
The proposed framework has the potential to complement the current
clinical routine by providing rapid on-site evaluation (ROSE) of
thrombi. H&E staining and inspection of thrombi remains a solid gold
standard in most clinical institutes owing to the effectiveness that
results from abundant domain knowledge. Our noninvasive test can be
integrated into this routine slide inspection workflow without
perturbing or prohibiting the existing processes. The use of label-free
ODT enabled us to reduce the time and associated effort of staining. Our
approach does not require any staining and can directly assess the
unlabeled specimen, allowing rapid histological analysis of patients
with AIS. Our approach can also provide consistent results compared with
conventional methods that suffer from staining variability and
interpreter fatigue.
The conventional composition assessment by practitioners depends heavily
on the color distribution which results from
staining.[7] As the staining procedures rely on
human or environmental conditions, the color distribution may be
significantly different.[9,41,42] By contrast, it
does not apply to our label-free approach that measures RI, an intrinsic
physical quantity of the native sample.
There are technical limitations in this work, such as the bulkiness and
complexity of the ODT hardware implementation, the limit in imaging
speed arising from the small FOV compared to a whole slide, and the
acquisition rate of the image sensor. However, recent developments in
non-interferometric ODT using low-coherence sources and engineering
approaches such as multiplexing can mitigate these
issues.[43,44] Other possible engineering
approaches include multiplexing which expands the measurement bandwidth
by exploiting the polarization[45] or spectral
dimension.[46]
5 CONCLUSIONS
We proposed and experimentally demonstrated a rapid and fully automated
prediction of thrombus composition using label-free refractive index
(RI) tomography and deep learning. Our proposed convolutional neural
network model accurately classified thrombus subtypes for each
label-free RI patch with high accuracy (>95%), affording
quantitative analysis of unstained thrombus slides. We believe that this
approach, which does not require any staining or manual inspection, will
significantly accelerate thrombus biopsy procedures for diagnosing
ischemic stroke and related diseases.
In future work, we may extend our framework to fully exploit the 3D
information of the RI tomogram, which could enhance the accuracy of the
thrombus composition inference. Our DL-based approach using label-free
ODT images presents a promising alternative to conventional
staining-based methods for histological analysis of thrombi, which can
also be further extended to identify and classify cell types in tissue
slides. The proposed framework has the potential to provide more
efficient and consistent analysis while complementing the existing
clinical routine.