{"title":"基于深度学习的血管内超声图像中腔体和中外膜提取方法。","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":"{\"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. 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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). 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引用次数: 10
摘要
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。
A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images.
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.
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