## 5 Discussion  
Most methods presented in Section 2 do not consider all the plaque types that can be identified in OCT images by medical experts [9]. More often, these methods do not distinguish fibrous plaque. Our study includes two more types of plaque than the previously mentioned works [22; 23]. Specifically, we considered four plaque types: fibrous, lipid, calcific, and mixed. A-line classification methods were based on a quite arbitrary outer wall border segmentation to select the input (A-line patch) to the classifier. Alternatively, other methods used whole A-lines (from the catheter to maximal depth). We selected A-line patches that start from the arterial lumen-wall border and include pixels that are part of the arterial wall. Instead of choosing an endpoint pixel at a certain depth, we compared the classification results of our pipeline giving as input A-line patches of different sizes. In addition, inspired by the work of [PERSON] et al. [26] and using [PERSON] et al. [29] approach, we estimated the attenuation coefficient in every pixel of each image. The outcome images offered better A-line classification results than the images based on original intensities. Finally, the CNN architecture and the various training options we proposed were the result of testing, OCT-specific intuition, and adoption of ideas that we considered to fit the problem. More common methods of splitting the image into square patches were not preferred. Instead, A-lines were chosen because the stratification of the tissue of the arterial wall is the major anatomic feature of the plaque. Concerning A-line segment selection, our estimation is that it is better to extract the more meaningful part that light can be backscattered than the whole A-line used in other studies. The co-occurrence of two plaques in the same wall part and the extent of plaques were not examined in this work since this adds high variability in classes. In the case of co-occurrence of plaques, the dataset is too small for  
Figure 8: The confusion matrix of A-line classification: 1. lipid; 2. mixed; 3. fibrous; 4. calcified plaque. reliable classification Further, the problem of OCT penetration in the vessel wall depending on the OCT technology used is an issue we need to take into account when developing four class classifiers. The data examined are single frames, and after extended testing using ALEXnet, we achieved the results presented in this paper. Of course, a deep learning approach is desirable to be applied on a large set of data so that the evaluation of the deep learning approach can reach as high as possible performance metrics. Given the size of the data we used, we believe that the proposed architecture achieved the best possible evaluation metrics. Further, various data augmentation techniques are not a favorable option because of the specific nature of OCT A-lines. The generative adversarial network (GAN) for data augmentation may be a suitable option, and it can be employed in future work given the availability of more data. In this case, we believe that it is best to add in vivo images, which offer real and not synthetic variability, to the dataset. One other aspect of the data to be considered is that by transforming images to polar coordinates, images are represented with their original acquisition form. The method could be applied to raw data (possibly with better results since calibration markers and going back and forth to coordinates' systems introduces errors). One more important point to stress is that computer vision algorithms' efficiency can be enhanced with specialized pre-processing relevant to the modality, especially in the case of medical images. The attenuation properties of the tissue were examined in this study. Backscattering properties could be examined too, but according to [29], backscattering coefficients in homogeneous regions are linearly related to the attenuation coefficients. Experiments were carried out with the same setup for attenuation images and for original images.