Phenotyping calcification in vascular tissues using artificial intelligence
Phenotyping calcification in vascular tissues using artificial intelligence
复制标题
使用人工智能对血管组织中的钙化进行表型分析
DOI：
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复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
J. Cebral
中科院分区：
文献类型：
--
作者：
Mehdi Ramezanpour;Anne M. Robertson;Yasutaka Tobe;Xiaowei Jia;J. Cebral
血管钙化是导致心脏病和中风等主要不良心血管事件 (MACE) 的重要因素。关于如何将不同形式的血管钙化整合到临床风险评估工具中仍然存在争议。即使是常用的冠状动脉钙评分（假设风险与总钙化成正比）也存在严重的不一致。需要进行基础研究来确定不同钙化表型如何影响风险。然而，由于缺乏用于对成像数据集中的钙化进行分类的高通量、客观和非破坏性工具，此类研究受到阻碍。在这里，我们引入了一种新的钙化表型分类系统以及半自动化、非破坏性的流程，甚至可以在动脉粥样硬化组织中区分这些表型。该管道包括一个基于深度学习的框架，用于分割嘈杂的 μ-CT 图像中的脂质池，以及一个无监督的聚类框架，用于根据大小、聚类和拓扑对钙化进行分类。该方法以 5 个血管标本为例进行了说明，可在不到 7 小时的时间内对多达 3200 张图像中的数千个钙化颗粒进行表型分析。组织和脂质池的平均 Dice 相似系数分别为 0.96 和 0.87，尽管这些组织具有很高的异质性，但仅需要 13 张图像进行训练和验证。通过引入一种有效且全面的钙化表型分析方法，这项工作使大规模研究能够确定更可靠的心血管事件风险指标，而心血管事件是全球死亡率和发病率的主要原因。
Vascular calcification is implicated as an important factor in major adverse cardiovascular events (MACE), including heart attack and stroke. A controversy remains over how to integrate the diverse forms of vascular calcification into clinical risk assessment tools. Even the commonly used calcium score for coronary arteries, which assumes risk scales positively with total calcification, has important inconsistencies. Fundamental studies are needed to determine how risk is influenced by the diverse calcification phenotypes. However, studies of these kinds are hindered by the lack of high-throughput, objective, and non-destructive tools for classifying calcification in imaging data sets. Here, we introduce a new classification system for phenotyping calcification along with a semi-automated, non-destructive pipeline that can distinguish these phenotypes in even atherosclerotic tissues. The pipeline includes a deep-learning-based framework for segmenting lipid pools in noisy μ-CT images and an unsupervised clustering framework for categorizing calcification based on size, clustering, and topology. This approach is illustrated for five vascular specimens, providing phenotyping for thousands of calcification particles across as many as 3200 images in less than seven hours. Average Dice Similarity Coefficients of 0.96 and 0.87 could be achieved for tissue and lipid pool, respectively, with training and validation needed on only 13 images despite the high heterogeneity in these tissues. By introducing an efficient and comprehensive approach to phenotyping calcification, this work enables large-scale studies to identify a more reliable indicator of the risk of cardiovascular events, a leading cause of global mortality and morbidity.
DOI：
10.1152/ajpheart.00036.2012
发表时间：
2012-09-01
影响因子：
4.8
作者：
Maldonado, Natalia;Kelly-Arnold, Adreanne;Weinbaum, Sheldon
通讯作者：
Weinbaum, Sheldon