在血管内光学相干断层成像中有效的自主腔分割:揭示多项式-回归卷积神经网络的潜力

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL Irbm Pub Date : 2023-11-22 DOI:10.1016/j.irbm.2023.100814
Yu Shi Lau , Li Kuo Tan , Kok Han Chee , Chow Khuen Chan , Yih Miin Liew
{"title":"在血管内光学相干断层成像中有效的自主腔分割:揭示多项式-回归卷积神经网络的潜力","authors":"Yu Shi Lau ,&nbsp;Li Kuo Tan ,&nbsp;Kok Han Chee ,&nbsp;Chow Khuen Chan ,&nbsp;Yih Miin Liew","doi":"10.1016/j.irbm.2023.100814","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p><span>Intravascular optical coherence tomography (IVOCT) is a crucial micro-resolution </span>imaging modality<span> used to assess the internal structure of blood vessels. Lumen segmentation in IVOCT images is vital for measuring the location and the extent of vessel blockages and for guiding percutaneous coronary intervention. Obtaining such information in real-time is essential, necessitating the use of fast automated algorithms. In this paper, we proposed an innovative polynomial-regression convolutional neural network (CNN) for fast and automated IVOCT lumen segmentation.</span></p></div><div><h3>Materials and methods</h3><p>The polynomial-regression CNN architecture was uniquely crafted to enable single-pass extraction of lumen borders via IVOCT image regression, ensuring real-time processing efficiency without compromising accuracy. The architecture designed convolution for regression while omitting fully connected layers, leading to the spatial output of lumen representation as polynomial coefficients, thus enabling the formation of interconnected lumen points. The approach equipped the network to comprehend the intricate and continuous geometries and curvatures intrinsic to blood vessels in transverse and longitudinal dimensions. The network was trained on a dataset of 16,165 images and evaluated using 7,016 images.</p></div><div><h3>Results</h3><p>The predicted segmentations exhibited a distance error of less than 2 pixels (26.40 μm), Dice's coefficient of 0.982, Jaccard Index of 0.966, sensitivity of 0.980, specificity of 0.999, and a prediction time of 4 s (for a pullback containing 360 images). This technique demonstrated significantly improved performance in both accuracy and speed compared to published techniques.</p></div><div><h3>Conclusion</h3><p>The strong segmentation performance, fast speed, and robustness to image variations highlight the practical clinical utility of the proposed polynomial-regression network.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient Autonomous Lumen Segmentation in Intravascular Optical Coherence Tomography Images: Unveiling the Potential of Polynomial-Regression Convolutional Neural Network\",\"authors\":\"Yu Shi Lau ,&nbsp;Li Kuo Tan ,&nbsp;Kok Han Chee ,&nbsp;Chow Khuen Chan ,&nbsp;Yih Miin Liew\",\"doi\":\"10.1016/j.irbm.2023.100814\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p><span>Intravascular optical coherence tomography (IVOCT) is a crucial micro-resolution </span>imaging modality<span> used to assess the internal structure of blood vessels. Lumen segmentation in IVOCT images is vital for measuring the location and the extent of vessel blockages and for guiding percutaneous coronary intervention. Obtaining such information in real-time is essential, necessitating the use of fast automated algorithms. In this paper, we proposed an innovative polynomial-regression convolutional neural network (CNN) for fast and automated IVOCT lumen segmentation.</span></p></div><div><h3>Materials and methods</h3><p>The polynomial-regression CNN architecture was uniquely crafted to enable single-pass extraction of lumen borders via IVOCT image regression, ensuring real-time processing efficiency without compromising accuracy. The architecture designed convolution for regression while omitting fully connected layers, leading to the spatial output of lumen representation as polynomial coefficients, thus enabling the formation of interconnected lumen points. The approach equipped the network to comprehend the intricate and continuous geometries and curvatures intrinsic to blood vessels in transverse and longitudinal dimensions. The network was trained on a dataset of 16,165 images and evaluated using 7,016 images.</p></div><div><h3>Results</h3><p>The predicted segmentations exhibited a distance error of less than 2 pixels (26.40 μm), Dice's coefficient of 0.982, Jaccard Index of 0.966, sensitivity of 0.980, specificity of 0.999, and a prediction time of 4 s (for a pullback containing 360 images). This technique demonstrated significantly improved performance in both accuracy and speed compared to published techniques.</p></div><div><h3>Conclusion</h3><p>The strong segmentation performance, fast speed, and robustness to image variations highlight the practical clinical utility of the proposed polynomial-regression network.</p></div>\",\"PeriodicalId\":14605,\"journal\":{\"name\":\"Irbm\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2023-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Irbm\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1959031823000635\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Irbm","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1959031823000635","RegionNum":4,"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

摘要

目的血管内光学相干断层扫描(IVOCT)是一种用于评估血管内部结构的关键微分辨率成像方式。IVOCT图像中的管腔分割对于测量血管阻塞的位置和程度以及指导经皮冠状动脉介入治疗至关重要。实时获取这些信息是必不可少的,因此需要使用快速的自动算法。在本文中,我们提出了一种创新的多项式回归卷积神经网络(CNN),用于快速和自动的IVOCT腔体分割。材料和方法采用独特的多项式回归CNN架构,通过IVOCT图像回归实现单次提取流腔边界,确保实时处理效率而不影响精度。该架构设计了用于回归的卷积,同时省略了完全连接的层,导致流明表示作为多项式系数的空间输出,从而形成相互连接的流明点。该方法使网络能够理解血管在横向和纵向上固有的复杂和连续的几何形状和曲率。该网络在包含16,165张图像的数据集上进行训练,并使用7,016张图像进行评估。结果预测的分割距离误差小于2像素(26.40 μm), Dice系数为0.982,Jaccard指数为0.966,灵敏度为0.980,特异性为0.999,预测时间为4 s(对于包含360张图像的回拉)。与已发表的技术相比,该技术在准确性和速度方面都有了显著提高。结论所提出的多项式回归网络具有较强的分割性能、较快的速度和对图像变化的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Efficient Autonomous Lumen Segmentation in Intravascular Optical Coherence Tomography Images: Unveiling the Potential of Polynomial-Regression Convolutional Neural Network

Objectives

Intravascular optical coherence tomography (IVOCT) is a crucial micro-resolution imaging modality used to assess the internal structure of blood vessels. Lumen segmentation in IVOCT images is vital for measuring the location and the extent of vessel blockages and for guiding percutaneous coronary intervention. Obtaining such information in real-time is essential, necessitating the use of fast automated algorithms. In this paper, we proposed an innovative polynomial-regression convolutional neural network (CNN) for fast and automated IVOCT lumen segmentation.

Materials and methods

The polynomial-regression CNN architecture was uniquely crafted to enable single-pass extraction of lumen borders via IVOCT image regression, ensuring real-time processing efficiency without compromising accuracy. The architecture designed convolution for regression while omitting fully connected layers, leading to the spatial output of lumen representation as polynomial coefficients, thus enabling the formation of interconnected lumen points. The approach equipped the network to comprehend the intricate and continuous geometries and curvatures intrinsic to blood vessels in transverse and longitudinal dimensions. The network was trained on a dataset of 16,165 images and evaluated using 7,016 images.

Results

The predicted segmentations exhibited a distance error of less than 2 pixels (26.40 μm), Dice's coefficient of 0.982, Jaccard Index of 0.966, sensitivity of 0.980, specificity of 0.999, and a prediction time of 4 s (for a pullback containing 360 images). This technique demonstrated significantly improved performance in both accuracy and speed compared to published techniques.

Conclusion

The strong segmentation performance, fast speed, and robustness to image variations highlight the practical clinical utility of the proposed polynomial-regression network.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
期刊最新文献
Mechanical Work and Metabolic Cost of Walking with Knee-Foot Prostheses: A Study with a Prosthesis Simulator Corrigendum to “Transition Network-Based Analysis of Electrodermal Activity Signals for Emotion Recognition” [IRBM 45 (2024) 100849] Editorial Board Contents An Ensemble Learning System Based on Stacking Strategy for Survival Risk Prediction of Patients with Esophageal Cancer
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1