## 1 Introduction  
Atherosclerosis is the main cause of morbidity and mortality in developed countries. It is a complex, progressive disease of the arterial wall, resulting in life-threatening thrombotic episodes such as acute coronary syndromes. More than 75% of plaques that result in acute myocardial infarction and/or death are non-stenoic [1]. Numerous studies based on histology and intravascular imaging techniques have tried to detect the type of atherosclerotic plaque that results in an acute coronary syndrome, whose most frequent mechanism is plaque rupture [1, 2]. This prone-to-rupture type of plaque is characterized as \"high risk\" and has specific morphologic features: a thin fibrous cap that covers a large superficial lipid pool and often exhibits local signs of inflammation [2, 3, 4]. Optical coherence tomography (OCT, intravascular OCT) is a light-based medical imaging modality. It was first demonstrated by [PERSON] et al. in 1991 [5]. In intravascular imaging, OCT has been established as the frequency or Fourier domain (FD--OCT) system due to the advantages it presents (in terms of speed and sensitivity) [6; 7]. In recent years, continuous improvements in the technology of OCT systems, mainly in terms of the Micro-Electro-Mechanical Systems (MEMS) [8], have allowed their easy application in the study of biological tissues and the achievement of an axial resolution <2 \(\upmu\)m.  
In Intravascular Optical coherence tomography, the light is in the near infrared (NIR) range, typically with wavelengths of approximately 1.3 \(\upmu\)m, which provides a 10-fold higher spatial resolution than that of intravascular ultrasound (IVUS), allowing the precise measurement of the fibrous cap thickness and detection of plaque components. OCT can be used to identify various stages of plaque morphology. The main limitation of intravascular OCT is the limited penetration depth in the artery wall, as it depends on tissue type and usually ranges from 0.1 to 2.0 mm using typical IVOCT NIR light. Intravascular OCT can identify several types of atherosclerotic plaques. According to the published expert consensus documents, an atherosclerotic plaque (atheroma) in intravascular OCT images is defined as a mass lesion (focal thickening) or loss of a layered structure of the vessel wall. A fibrous plaque has high backscattering and a relatively homogeneous intravascular OCT signal. A calcific plaque contains intravascular OCT evidence of calcium that appears as a signal-poor or heterogeneous region with a sharply delineated border (leading, trailing, and/or lateral edges). This definition applies to larger calcifications. A lipid plaque by intravascular OCT is a signal-poor region with poorly delineated borders, a fast signal drop-off and little or no signal backscattering, covered by a fibrous cap. Mixed lesions are called heterogeneous plaques or mixed lesions [9; 10]. The assessment of the degree of the development and the type of plaque is very important for identifying high-risk plaques. The use of OCT involves the collection of a large amount of imaging data (e.g., 100-300 cross-sectional images are collected for each artery), the examination of which requires a lot of time and great effort from a skilled medical specialist. Besides, it can be very hard to distinguish plaques even for experts on the field [9; 11]. Therefore, an automated method is needed to detect and categorize the microstructures of interest and objectify the characterization. [PERSON] et al. [12] demonstrated that automatic characterization methods can provide accurate results despite the propagation of error caused by the image formation, the intermediate steps, and the final classification phase of such methods. The aim of this work is the development of a new deep learning method to detect atherosclerotic tissues in intravascular OCT images. Steps for the classification of detected plaques into four categories are proposed.