## 4 Results
### Classification to Plaque or Normal Tissue
#### 4.1.3 Visualization of Results and Annotation Issues  
During annotation, only plaque regions were manually segmented. The rest of the regions of the arterial wall were considered normal. However, this may not always be the case. Medical experts annotate what they consider as most interesting plaque regions, e.g., a calcified plaque can be adjacent to fibrous plaque, which is extended to more A-lines than calcified plaque. In this case, A-lines corresponding to fibrous plaque may be wrongly classified as normal in our initial setup. This is in accordance with the results in Figure 6, where one can observe specific patterns of appearance in normal A-lines (true negative).  
\begin{table}
\begin{tabular}{c c c c} \hline
**A-Line Classification** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Original Images & 73.73\% \(\pm\) 5.65\% & 88.68\% \(\pm\) 1.41\% & 54.03\% \(\pm\) 10.25\% \\ Attenuation Images & 74.73\% \(\pm\) 6.55\% & 87.78\% \(\pm\) 8.79\% & 61.45\% \(\pm\) 8.4141\% \\ \hline \end{tabular}
\end{table}
Table 2: The mean test accuracy (sensitivity/specificity) \(\pm\) their standard deviation of A-lines (1 \(\times\) 90) classification for original images and attenuation images.  
\begin{table}
\begin{tabular}{c c c} \hline
**A-Line Classification** & **Simple Shallow Architecture** & **Deeper Architecture** \\ \hline Original Images & 78.43\% & 78.92\% \\ Attenuation Images & 82.52\% & 78.59\% \\ \hline \end{tabular}
\end{table}
Table 3: The best test accuracy of A-lines (1 \(\times\) 90) for original images and attenuation images.  
These patterns are absent in A-lines that were classified as plaque but are not initially annotated as such (false positive). Therefore, a number of these A-lines may be part of non-annotated plaque or an artifact. To cope with these cases, an active learning process is proposed by [PERSON] et al. [23]. In such a case, other medical experts blind to the initial expert annotation re-evaluate the automated method's result. Still, even after applying the active learning process, there are many challenges to be addressed in the manual annotation of OCT images in both research and clinical practice [7].  
Figure 7 shows plaque detection results for intravascular OCT test images containing three different plaque types. Visual inspection of the results showed that most A-lines that are falsely not classified as plaque are part of the fibrous plaque class. It should be noted that fibrous plaque having a similar texture with normal tissue is usually more difficult to discriminate from the latter. In general, however, there was a high degree of agreement between automatic and manual segmentation in locating the arteriosclerotic plaques.  
Figure 6: (**a–c**): A-lines classified as normal (true negative: examples in which the model correctly predicts the negative class = normal tissue). These images show the arterial wall (arrows) as a continuous and undisturbed zone, which is an indication of the normality of arterial tissue. (**d–f**): A-lines automatically classified as abnormal but without being manually annotated as atherosclerotic plaques (false positive: examples in which the model incorrectly predicts the positive class = abnormal tissue). However, these images show an asymmetric expansion and loosening of the arterial wall continuity as an area of intense heterogeneity, which can be interpreted as abnormal tissue. It should be noted that in medical diagnostic terminology, the positive class is identical to the abnormal state (abnormal tissue) and the negative class to the normal state (normal tissue).  
Although plaque regions may be missed by manual segmentation, it is considered that those that are delineated are accurately labeled as fibrous, calcified, mixed, or lipid. Therefore, we examined the classification in the latter four plaque types, excluding the normal A-lines.