Automated pericardium segmentation and epicardial adipose tissue quantification from computed tomography images

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control Pub Date : 2024-11-05 DOI:10.1016/j.bspc.2024.107167

Ying Wang , Ankang Wang , Lu Wang , Wenjun Tan , Lisheng Xu , Jinsong Wang , Songang Li , Jinshuai Liu , Yu Sun , Benqiang Yang , Steve Greenwald

{"title":"Automated pericardium segmentation and epicardial adipose tissue quantification from computed tomography images","authors":"Ying Wang , Ankang Wang , Lu Wang , Wenjun Tan , Lisheng Xu , Jinsong Wang , Songang Li , Jinshuai Liu , Yu Sun , Benqiang Yang , Steve Greenwald","doi":"10.1016/j.bspc.2024.107167","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Objective</h3><div>Epicardial Adipose Tissue (EAT) is regarded as an independent risk factor for cardiovascular disease, and an increase in its volume is closely associated with disorders such as coronary artery atherosclerosis. Traditional manual and semi-automatic methods for EAT segmentation rely on subjective judgment, resulting in uncertainty and unreliability, which limits their application in clinical practice. Therefore, this study aims to develop a fully automatic segmentation and quantification method to improve the accuracy of EAT assessment.</div></div><div><h3>Methods</h3><div>A Boundary-Enhanced Multi-scale U-Net network with a Convolutional Transformer (BMT-UNet) is developed to segment the pericardium. The BMT-UNet comprises Boundary-Enhanced (BE) modules, Multi-Scale (MS) modules, and a Convolutional Transformer (ConvT) module. The MS and BE modules in the encoding part are designed to capture detailed boundary features and accurately delineate the pericardium boundary by combining multi-scale features with morphological operations, leveraging their complementarity. The ConvT module integrates global contextual information, thereby enhancing overall segmentation accuracy and addressing the issue of internal holes in the segmented pericardial images. The volume of EAT is automatically quantified using standard fat thresholds with a range of −190 to −30 HU.</div></div><div><h3>Results</h3><div>For a Coronary Computed Tomography Angiography (CCTA) dataset which contained 50 patients, the Dice coefficient and Hausdorff distance for the proposed method of pericardial and EAT segmentation are 98.3% ± 0.2%, 5.7±0.8 mm, and 93.9% ± 1.7%, 2.1 ± 0.3 mm, respectively. The linear regression coefficient between the EAT volume segmented and the actual volume is 0.982, and the Pearson correlation coefficient is 0.99. Bland-Altman analysis further confirmed the high consistency between the automated and manual methods. These results demonstrate a significant improvement over existing methods, particularly in terms of segmentation precision and reliability, which are critical for clinical application.</div></div><div><h3>Conclusions</h3><div>This work develops an automated method for quantifying EAT in Computed Tomography (CT) images, and the results agreed closely with expert evaluations. Code is available at: <span><span>https://github.com/wy-9903/BMT-UNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"100 ","pages":"Article 107167"},"PeriodicalIF":4.9000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424012254","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}

引用次数: 0

Abstract

Background and Objective

Epicardial Adipose Tissue (EAT) is regarded as an independent risk factor for cardiovascular disease, and an increase in its volume is closely associated with disorders such as coronary artery atherosclerosis. Traditional manual and semi-automatic methods for EAT segmentation rely on subjective judgment, resulting in uncertainty and unreliability, which limits their application in clinical practice. Therefore, this study aims to develop a fully automatic segmentation and quantification method to improve the accuracy of EAT assessment.

Methods

A Boundary-Enhanced Multi-scale U-Net network with a Convolutional Transformer (BMT-UNet) is developed to segment the pericardium. The BMT-UNet comprises Boundary-Enhanced (BE) modules, Multi-Scale (MS) modules, and a Convolutional Transformer (ConvT) module. The MS and BE modules in the encoding part are designed to capture detailed boundary features and accurately delineate the pericardium boundary by combining multi-scale features with morphological operations, leveraging their complementarity. The ConvT module integrates global contextual information, thereby enhancing overall segmentation accuracy and addressing the issue of internal holes in the segmented pericardial images. The volume of EAT is automatically quantified using standard fat thresholds with a range of −190 to −30 HU.

Results

For a Coronary Computed Tomography Angiography (CCTA) dataset which contained 50 patients, the Dice coefficient and Hausdorff distance for the proposed method of pericardial and EAT segmentation are 98.3% ± 0.2%, 5.7±0.8 mm, and 93.9% ± 1.7%, 2.1 ± 0.3 mm, respectively. The linear regression coefficient between the EAT volume segmented and the actual volume is 0.982, and the Pearson correlation coefficient is 0.99. Bland-Altman analysis further confirmed the high consistency between the automated and manual methods. These results demonstrate a significant improvement over existing methods, particularly in terms of segmentation precision and reliability, which are critical for clinical application.

Conclusions

This work develops an automated method for quantifying EAT in Computed Tomography (CT) images, and the results agreed closely with expert evaluations. Code is available at: https://github.com/wy-9903/BMT-UNet.

查看原文本刊更多论文

从计算机断层扫描图像自动分割心包和量化心外膜脂肪组织

背景和目的心外膜脂肪组织（EAT）被认为是心血管疾病的独立危险因素，其体积的增加与冠状动脉粥样硬化等疾病密切相关。传统的手动和半自动 EAT 分割方法依赖于主观判断，存在不确定性和不可靠性，限制了其在临床实践中的应用。因此，本研究旨在开发一种全自动的心包分割和量化方法，以提高 EAT 评估的准确性。BMT-UNet 由边界增强 (BE) 模块、多尺度 (MS) 模块和卷积变换器 (ConvT) 模块组成。编码部分的 MS 和 BE 模块旨在捕捉详细的边界特征，并通过将多尺度特征与形态学操作相结合，利用它们之间的互补性，准确划分心包边界。ConvT 模块整合了全局上下文信息，从而提高了整体分割的准确性，并解决了心包图像分割后出现内孔的问题。结果在包含 50 名患者的冠状动脉计算机断层扫描（CCTA）数据集中，所提出的心包和 EAT 分割方法的 Dice 系数和 Hausdorff 距离分别为 98.3% ± 0.2%、5.7±0.8 mm 和 93.9% ± 1.7%、2.1±0.3 mm。EAT分割体积与实际体积的线性回归系数为0.982，皮尔逊相关系数为0.99。Bland-Altman 分析进一步证实了自动方法和人工方法之间的高度一致性。这些结果表明，与现有方法相比，该方法有了很大改进，尤其是在分割精度和可靠性方面，而这两点对于临床应用至关重要。代码见：https://github.com/wy-9903/BMT-UNet。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Biomedical Signal Processing and Control 工程技术-工程：生物医学

CiteScore

9.80

自引率

13.70%

发文量

822

审稿时长

4 months

期刊介绍： Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.