Analysis of Atherosclerotic Plaques Using OCT Images Based on Deep Learning: A Comprehensive Review

被引：0
作者：
Xue, Hao [1 ]
Hamed, Haza Nuzly Bin Abdull [1 ]
Isyaku, Babangida [2 ]
Su, Qichen [1 ]
Xin, Dai [1 ]
机构：
[1] Univ Teknol Malaysia Johor, Fac Comp, Johor Baharu 81310, Malaysia
[2] Sule Lamido Univ, Fac Informat Commun Technol, K Hausa 048, Jigawa State, Nigeria
来源：
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | 2024年 / 18卷 / 11期
关键词：
Atherosclerotic plaques; Optical coherence tomography; Detection; Segmentation; Classification;
D O I：
10.3837/tiis.2024.11.009
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Coronary atherosclerotic plaques are a leading cause of morbidity and mortality in coronary artery disease. Currently, optical coherence tomography (OCT) is employed by physicians to visualize intravascular plaques, a process that is labor-intensive and highly dependent on clinical judgment. Deep learning-based computer-aided techniques can effectively address these challenges. However, to date, no review has provided a comprehensive survey of these methods to direct future developments in the field. This review adopts two novel perspectives, namely the research task and the research question, which together address this gap in the literature by offering an in-depth discussion of 37 journal publications. Regarding tasks, this manuscript categorizes plaque research into three areas, which include detection, segmentation, and classification. In addition to analyzing and summarizing the benefits, drawbacks, and assessment metrics of deep learning models for each task, this section provides an overview of the issues in the field. The causes and solutions for the four challenges of noise, data shortage, data imbalance, and complex network structure are discussed in another topic. By providing a clear summary of the limitations in current research and suggesting future directions, this review aims to offer valuable insights to researchers working on coronary plaque analysis.
引用
收藏
页码：3256 / 3277
页数：22
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