## 2 Related Work  
Several methods attempted to automatically detect specific plaque regions. [PERSON] et al. [13] proposed an automatic method for lumen segmentation and calcified plaque detection. The first task is achieved by a dynamic programming scheme and the latter by combining edge detection with active contour models. Other approaches apply machine learning techniques based on hand-crafted or automatically extracted features. Specifically, [PERSON] et al. [14] used hand-crafted features to train Support Vector Machines (SVM) and Radial Basis Function (RBF) classifiers to discriminate calcifications, lipid plaques, and fibrous tissue/plaque(one grouped category). Similarly, [PERSON] et al. [15] used hand-crafted features to train SVM, neural networks and Random Forests (RF) classifiers to classify the detected plaque into four types: calcified, lipid, mixed, and fibrous tissue. Random Forests provided the best classification results. It is not clear, however, if the last type refers to fibrous plaque or normal fibrous tissue. [PERSON] et al. [16] proposed an unsupervised classification approach that uses the K-means clustering algorithm applied to statistical texture features to identify background, plaque, and normal tissue areas. Their method provided a rough estimation and was evaluated by visual inspection. Instead of using hand-crafted features, other studies applied deep learning to OCT cross-sections or patches, attempting a semantic interpretation. [PERSON] et al. [17] used ResNet and DenseNet to classify OCT images in three categories: normal with calcified plaque and with fibrous or lipid plaque. This method does not classify image regions but whole images. [PERSON] et al. [18] applied a fully connected CNN known as SegNet. The dataset consisted of 51 images of 13 patients. Patches of 100 \(\times\) 100 pixels were input to SegNet, which produced a probability map with two classes: normal and calcified plaque. Probabilities derived from overlapping patches were combined in post-processing steps to classify each pixel of the image. The accuracy was 0.7 in this task. [PERSON] et al. [19] presented a method that applied an original CNN to 269 images of 22 patients. Images were divided into square patches of 51 \(\times\) 51 pixels, which were used to train the network. They chose to classify tissue in 4 categories (neglecting fibrous plaque). The accuracy of the algorithm in detecting calcified plaque was too low, resulting in Dice = 0.22. Most of the existing methods are patch-based (pixel classification), having several limitations. Plaque types are not considered, e.g., fibrous plaque. Moreover, it is uncertain if these methods can generalize to other images because there is no reference to data handling and splitting into training, validation, and test sets. Generally, patch-based approaches have difficulty achieving tissue classification because some plaques present similar textures. In addition, the stratification of artery wall layers (intima, media, and adventitia), which is not depicted in patches, is more indicative of the presence of plaque structures. Therefore, several methods were proposed for axial line (A-line) classification. A-line classification does not permit the delineation of plaque structures, which is attempted by patch-based methods. However, it permits the estimation of the extent of the plaque present on the arterial wall. [PERSON] et al. [20] modeled every axial line in IVOCT data as a linear combination of several depth profiles. After the estimation of these profiles through least-square optimization, they classified the tissue types based on their morphological features. While they examine four A-line types--Intinal-thickening, Fibrotic, Superficial-Lipid, and Fibrotic-Lipid--their method was tested in ex vivo data and was successful in discriminating lipid and non-lipid tissue (85%). [PERSON] et al. [21] applied dual binary classifiers and SVM to features extracted from A-lines to achieve 81.56% accuracy in three-way classification. [PERSON] et al.