Affiliation:
1. Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China
2. Cardiovascular Department First Affiliated Hospital of Xi'an Jiaotong University China
Abstract
AbstractAutomated 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.
Funder
National Key Research and Development Program of China
Subject
General Physics and Astronomy,General Engineering,General Biochemistry, Genetics and Molecular Biology,General Materials Science,General Chemistry
Cited by
1 articles.
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