Ramezanpour Mehdi, Robertson Anne M, Tobe Yasutaka, Jia Xiaowei, Cebral Juan R
Department of Mechanical Engineering and Materials Science, University of Pittsburgh, PA, USA.
Department of Computer Science, University of Pittsburgh, PA, USA.
ArXiv. 2024 Jan 17:arXiv:2401.07825v2.
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.
血管钙化被认为是包括心脏病发作和中风在内的主要不良心血管事件（MACE）的一个重要因素。关于如何将多种形式的血管钙化纳入临床风险评估工具仍存在争议。即使是常用的冠状动脉钙化评分，其假设风险与总钙化呈正相关，也存在重要的不一致之处。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，这类研究受到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的阻碍。在这里，我们引入了一种用于钙化表型分析的新分类系统以及一个半自动、非破坏性的流程，该流程甚至可以在动脉粥样硬化组织中区分这些表型。该流程包括一个基于深度学习的框架，用于在有噪声的μ-CT图像中分割脂质池，以及一个无监督聚类框架，用于根据大小、聚类和拓扑结构对钙化进行分类。对五个血管标本展示了这种方法，在不到七小时的时间内为多达3200张图像中的数千个钙化颗粒提供了表型分析。尽管这些组织具有高度异质性，但仅需13张图像进行训练和验证，组织和脂质池的平均骰子相似系数分别可达0.96和0.87。通过引入一种高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识别出更可靠的心血管事件风险指标，心血管事件是全球死亡和发病的主要原因。
