Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China.
Cardiovascular Department, First Affiliated Hospital of Xi'an Jiaotong University, China.
J Biophotonics. 2023 Sep;16(9):e202300059. doi: 10.1002/jbio.202300059. Epub 2023 Jun 14.
Automated analysis of the vessel structure in intravascular optical coherence tomography (IVOCT) images is critical to assess the health status of vessels and monitor coronary artery disease progression. However, deep learning-based methods usually require well-annotated large datasets, which are difficult to obtain in the field of medical image analysis. Hence, an automatic layers segmentation method based on meta-learning was proposed, which can simultaneously extract the surfaces of the lumen, intima, media, and adventitia using a handful of annotated samples. Specifically, we leverage a bi-level gradient strategy to train a meta-learner for capturing the shared meta-knowledge among different anatomical layers and quickly adapting to unknown anatomical layers. Then, a Claw-type network and a contrast consistency loss were designed to better learn the meta-knowledge according to the characteristic of annotation of the lumen and anatomical layers. Experimental results on the two cardiovascular IVOCT datasets show that the proposed method achieved state-of-art performance.
血管内光学相干断层扫描（IVOCT）图像中血管结构的自动分析对于评估血管的健康状况和监测冠状动脉疾病的进展至关重要。然而，基于深度学习的方法通常需要经过充分注释的大型数据集，而这在医学图像分析领域很难获得。因此，提出了一种基于元学习的自动分层分割方法，该方法可以使用少量注释样本同时提取管腔、内膜、中膜和外膜的表面。具体来说，我们利用双级梯度策略来训练元学习者，以捕获不同解剖层之间的共享元知识，并快速适应未知的解剖层。然后，设计了一种爪形网络和对比一致性损失，根据管腔和解剖层的注释特征更好地学习元知识。在两个心血管 IVOCT 数据集上的实验结果表明，所提出的方法达到了最先进的性能。
