FIGURE 3. Optical measurement. (a) BF image and (b) RI focal
section. The boxed regions i, ii and iii denote mixed, RBC, and fibrin
regions respectively. The size of each patch is 10.88 µm × 10.88 µm.
Scale bars = 500 µm (boxed regions) and 50 µm (patches).
To validate the quality of the RI reconstruction, we visually compared
the thrombus structure in the paired RI tomogram section and BF image.
Figure 3 shows the BF and RI images of thrombus sample 3 at various
magnifications: whole-slide level, multiple patches level, and
individual patch level. The RI tomograms of the thrombus show various RI
value distributions ranging from 1.48 to 1.5. Compared to the BF image,
the RI tomogram section visualizes RBCs and fibrin structures of
thrombus components better. The contrast of RI is directly related to
the concentration of material.[36,37] In Figure 3,
the magnified region of interest (ROI) of sample 3 is presented. In the
RI tomogram section (Figure 3b), some borders of individual RBC are
clearly shown even in tightly compressed region, whereas in the
corresponding BF image (Figure 3a), the borders of individual RBCs are
unclear. Some RBC borders in the RI image possibly indicate
polyhedrocytes, which are tightly packed red blood cells (RBCs) with
polyhedral shapes formed when blood clots contract. In the fibrin
region, (iii) of Figure 3, the pores in the meshwork of fibrin fibers
are also better observed in the RI tomogram. By contrast, the boundary
of fibrin pores cannot be identified in the BF image.
3.2 Patch-wise Composition Prediction based on DL
Our dataset for DL-based prediction consisted of 166,887 patches of
focal RI sections, with a FOV of each patch at 10.88 μm × 10.88 μm. For
each slide, the patches were randomly separated into the training,
validation, and test sets in a ratio of 7:1.5:1.5.
The DL classifier was trained using the training and validation datasets
and blind-tested using the test dataset (Figure 4a ). The
optimal model was chosen based on the validation loss during DL
training, and the learning curve is presented in Figure 4b. The
resulting test accuracy of the DL model was 95.1% (Figure 4d). A major
error in the patch-wise classification was the misclassification of RBC
into fibrin, accounting for 10.4% of all the RBC patches in the test
set. This error could be due to the limited correspondence between the
two consecutive thrombus sections, resulting in imperfectly mixed
regions in the registered patch annotation. Additionally, the rate of
confusing fibrin with the background was higher (2.97%) than that of
confusing RBC with background (0.577%). This difference in
background-related error is consistent with our observations of thin
fibrin structures at the edge of the slide and the relatively low RI
distribution of fibrin.