{"title":"A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images.","authors":"Fubao Zhu, Zhengyuan Gao, Chen Zhao, Hanlei Zhu, Jiaofen Nan, Yanhui Tian, Yong Dong, Jingfeng Jiang, Xiaohong Feng, Neng Dai, Weihua Zhou","doi":"10.1177/01617346221114137","DOIUrl":null,"url":null,"abstract":"<p><p>Intravascular ultrasound (IVUS) imaging allows direct visualization of the coronary vessel wall and is suitable for assessing atherosclerosis and the degree of stenosis. Accurate segmentation and lumen and median-adventitia (MA) measurements from IVUS are essential for such a successful clinical evaluation. However, current automated segmentation by commercial software relies on manual corrections, which is time-consuming and user-dependent. We aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically and accurately extract both lumen and MA border. Inspired by the dual-path design of the state-of-the-art model IVUS-Net, our method named IVUS-U-Net++ achieved an extension of the U-Net++ model. More specifically, a feature pyramid network was added to the U-Net++ model, enabling the utilization of feature maps at different scales. Following the segmentation, the Pearson correlation and Bland-Altman analyses were performed to evaluate the correlations of 12 clinical parameters measured from our segmentation results and the ground truth. A dataset with 1746 IVUS images from 18 patients was used for training and testing. Our segmentation model at the patient level achieved a Jaccard measure (JM) of 0.9080 ± 0.0321 and a Hausdorff distance (HD) of 0.1484 ± 0.1584 mm for the lumen border; it achieved a JM of 0.9199 ± 0.0370 and an HD of 0.1781 ± 0.1906 mm for the MA border. The 12 clinical parameters measured from our segmentation results agreed well with those from the ground truth (all <i>p</i>-values are smaller than .01). Our proposed method shows great promise for its clinical use in IVUS segmentation.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ultrasonic Imaging","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/01617346221114137","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/7/21 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 10
Abstract
Intravascular ultrasound (IVUS) imaging allows direct visualization of the coronary vessel wall and is suitable for assessing atherosclerosis and the degree of stenosis. Accurate segmentation and lumen and median-adventitia (MA) measurements from IVUS are essential for such a successful clinical evaluation. However, current automated segmentation by commercial software relies on manual corrections, which is time-consuming and user-dependent. We aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically and accurately extract both lumen and MA border. Inspired by the dual-path design of the state-of-the-art model IVUS-Net, our method named IVUS-U-Net++ achieved an extension of the U-Net++ model. More specifically, a feature pyramid network was added to the U-Net++ model, enabling the utilization of feature maps at different scales. Following the segmentation, the Pearson correlation and Bland-Altman analyses were performed to evaluate the correlations of 12 clinical parameters measured from our segmentation results and the ground truth. A dataset with 1746 IVUS images from 18 patients was used for training and testing. Our segmentation model at the patient level achieved a Jaccard measure (JM) of 0.9080 ± 0.0321 and a Hausdorff distance (HD) of 0.1484 ± 0.1584 mm for the lumen border; it achieved a JM of 0.9199 ± 0.0370 and an HD of 0.1781 ± 0.1906 mm for the MA border. The 12 clinical parameters measured from our segmentation results agreed well with those from the ground truth (all p-values are smaller than .01). Our proposed method shows great promise for its clinical use in IVUS segmentation.
血管内超声(IVUS)成像可以直接显示冠状血管壁,适用于评估动脉粥样硬化和狭窄程度。IVUS准确的分割和管腔和中外膜(MA)测量对于这种成功的临床评估至关重要。然而,目前商业软件的自动分割依赖于人工校正,耗时且依赖于用户。我们的目标是开发一种基于深度学习的方法,使用编码器-解码器深度架构来自动准确地提取lumen和MA边界。受最先进的IVUS-Net模型双路径设计的启发,我们的方法IVUS-U-Net++实现了对U-Net++模型的扩展。更具体地说,在U-Net++模型中增加了一个特征金字塔网络,从而可以在不同的尺度上使用特征映射。分割后,进行Pearson相关性和Bland-Altman分析,以评估从分割结果和基本事实中测量的12个临床参数的相关性。来自18名患者的1746张IVUS图像数据集用于训练和测试。我们的分割模型在患者水平上实现了Jaccard测量(JM)为0.9080±0.0321,Hausdorff距离(HD)为0.1484±0.1584 mm;MA边界的JM为0.9199±0.0370 mm, HD为0.1781±0.1906 mm。从我们的分割结果中测量的12个临床参数与基础真实值一致(所有p值都小于0.01)。该方法在IVUS分割中具有广阔的临床应用前景。
期刊介绍:
Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging