• Laser & Optoelectronics Progress
  • Vol. 59, Issue 22, 2200002 (2022)
Zheng Sun1、2、* and Shuyan Wang1、2
Author Affiliations
  • 1Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, Hebei , China
  • 2Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, Hebei , China
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    DOI: 10.3788/LOP202259.2200002 Cite this Article
    Zheng Sun, Shuyan Wang. Application of Deep Learning in Intravascular Optical Coherence Tomography[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2200002 Copy Citation Text show less


    Intravascular optical coherence tomography (IVOCT) is a minimally invasive imaging model that currently has the highest resolution. It is capable of providing information of the vascular lumen morphology and near-microscopic structures of the vessel wall. For each pullback of the target vessel, hundreds or thousands of B-scan images are obtained in routine clinical applications. Manual image analysis is time-consuming and laborious, and the findings depend on the operators' professional ability in some sense. Recently, as deep learning technology has continuously made significant breakthroughs in the medical imaging field, it has also been used in the computer-aided automated analysis of IVOCT images. This study outlines the applications of deep learning in IVOCT, primarily involving image segmentation, tissue characterization, plaque classification, and object detection. The benefits and limitations of the existing approaches are discussed, and the future possible development is described.