Ziyu Cui, Zhaoju Zhu, Peiwen Huang, Chuhang Gao, Bingwei He
{"title":"An automatic segmentation of calcified tissue in forward-looking intravascular ultrasound images","authors":"Ziyu Cui, Zhaoju Zhu, Peiwen Huang, Chuhang Gao, Bingwei He","doi":"10.1016/j.bspc.2024.107095","DOIUrl":null,"url":null,"abstract":"<div><div>The assessment of images of the coronary artery system plays a crucial part in the diagnosis and treatment of cardiovascular diseases (CVD). Forward-looking intravascular ultrasound (FL-IVUS) has a distinct advantage in assessing CVD due to its superior resolution and imaging capability, especially in severe calcification scenarios. The demarcation of the lumen and media-adventitia, as well as the identification of calcified tissue information, constitute the initial steps in assessing of CVD such as atherosclerosis using FL-IVUS images. In this research, we introduced a novel approach for automated lumen segmentation and identification of calcified tissue in FL-IVUS images. The proposed method utilizes superpixel segmentation and fuzzy C-means clustering (FCM) to identify regions that potentially correspond to lumina. Furthermore, connected component labeling and active contour methods are employed to refine the contours of lumina. To handle the distinctive depth information found in FL-IVUS images, ellipse fitting and region detectors are applied to identify areas with calcified tissue. In our dataset consisting of 43 FL-IVUS images, this method achieved mean values for Jaccard measure, Dice coefficient, Hausdorff distance, and percentage area difference at 0.952 ± 0.016, 0.975 ± 0.008, 0.296 ± 0.186, and 0.019 ± 0.010, respectively. Furthermore, when compared with traditional segmentation approaches, the proposed approach yields higher images quality. The test results demonstrate the effectiveness of this innovative automated segmentation technique for detecting the lumina and calcified tissue in FL-IVUS images.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-10-28","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/S1746809424011534","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
The assessment of images of the coronary artery system plays a crucial part in the diagnosis and treatment of cardiovascular diseases (CVD). Forward-looking intravascular ultrasound (FL-IVUS) has a distinct advantage in assessing CVD due to its superior resolution and imaging capability, especially in severe calcification scenarios. The demarcation of the lumen and media-adventitia, as well as the identification of calcified tissue information, constitute the initial steps in assessing of CVD such as atherosclerosis using FL-IVUS images. In this research, we introduced a novel approach for automated lumen segmentation and identification of calcified tissue in FL-IVUS images. The proposed method utilizes superpixel segmentation and fuzzy C-means clustering (FCM) to identify regions that potentially correspond to lumina. Furthermore, connected component labeling and active contour methods are employed to refine the contours of lumina. To handle the distinctive depth information found in FL-IVUS images, ellipse fitting and region detectors are applied to identify areas with calcified tissue. In our dataset consisting of 43 FL-IVUS images, this method achieved mean values for Jaccard measure, Dice coefficient, Hausdorff distance, and percentage area difference at 0.952 ± 0.016, 0.975 ± 0.008, 0.296 ± 0.186, and 0.019 ± 0.010, respectively. Furthermore, when compared with traditional segmentation approaches, the proposed approach yields higher images quality. The test results demonstrate the effectiveness of this innovative automated segmentation technique for detecting the lumina and calcified tissue in FL-IVUS images.
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.