• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一种新型人体心脏血管识别、分割及三维重建机制。

A new human heart vessel identification, segmentation and 3D reconstruction mechanism.

作者信息

Al-Surmi Aqeel, Wirza Rahmita, Mahmod Ramlan, Khalid Fatimah, Dimon Mohd Zamrin

机构信息

Department of Multimedia, Faculty of Computer Science and Information Technology, University Putra Malaysia, Selangor, Malaysia.

Cardiothoracic Unit, Surgical Cluster, Faculty of Medicine, Universiti Teknologi MARA, Selangor, Malaysia.

出版信息

J Cardiothorac Surg. 2014 Oct 2;9:161. doi: 10.1186/s13019-014-0161-1.

DOI:10.1186/s13019-014-0161-1
PMID:25274253
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4190392/
Abstract

BACKGROUND

The identification and segmentation of inhomogeneous image regions is one of the most challenging issues nowadays. The surface vessels of the human heart are important for the surgeons to locate the region where to perform the surgery and to avoid surgical injuries. In addition, such identification, segmentation, and visualisation helps novice surgeons in the training phase of cardiac surgery.

METHODS

This article introduces a new mechanism for identifying the position of vessels leading to the performance of surgery by enhancement of the input image. In addition, develop a 3D vessel reconstruction out of a single-view of a real human heart colour image obtained during open-heart surgery.

RESULTS

Reduces the time required for locating the vessel region of interest (ROI). The vessel ROI must appear clearly for the surgeons. Furthermore, reduces the time required for training cardiac surgery of the novice surgeons. The 94.42% accuracy rate of the proposed vessel segmentation method using RGB colour space compares to other colour spaces.

CONCLUSIONS

The advantage of this mechanism is to help the surgeons to perform surgery in less time, avoid surgical errors, and to reduce surgical effort. Moreover, the proposed technique can reconstruct the 3D vessel model from a single image to facilitate learning of the heart anatomy as well as training of cardiac surgery for the novice surgeons. Furthermore, extensive experiments have been conducted which reveal the superior performance of the proposed mechanism compared to the state of the art methods.

摘要

背景

非均匀图像区域的识别与分割是当今最具挑战性的问题之一。人类心脏的表面血管对于外科医生定位手术区域和避免手术损伤至关重要。此外，这种识别、分割和可视化有助于处于心脏手术训练阶段的新手外科医生。

方法

本文介绍了一种通过增强输入图像来识别血管位置以指导手术操作的新机制。此外，从心脏直视手术期间获得的真实人类心脏彩色图像的单视图中重建三维血管。

结果

减少了定位感兴趣血管区域（ROI）所需的时间。血管ROI必须清晰地呈现给外科医生。此外，减少了新手外科医生心脏手术训练所需的时间。与其他颜色空间相比，所提出的使用RGB颜色空间的血管分割方法准确率达到94.42%。

结论

该机制的优点是帮助外科医生在更短的时间内进行手术，避免手术失误，并减少手术工作量。此外，所提出的技术可以从单幅图像重建三维血管模型，以方便新手外科医生学习心脏解剖结构以及进行心脏手术训练。此外，已进行的大量实验表明，与现有技术方法相比，所提出的机制具有卓越的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/b2c4169cf134/13019_2014_Article_161_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/0ca81deacda2/13019_2014_Article_161_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/a9f516c64753/13019_2014_Article_161_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/716fc176a97c/13019_2014_Article_161_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/f19ce3c5f33a/13019_2014_Article_161_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/5020f7c51194/13019_2014_Article_161_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/8a355e9fea9a/13019_2014_Article_161_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/c0ce67eceff9/13019_2014_Article_161_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/b272122a50b9/13019_2014_Article_161_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/a83caa48afb7/13019_2014_Article_161_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/349b1753d461/13019_2014_Article_161_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/e30e0264c1bc/13019_2014_Article_161_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/06c710ae337c/13019_2014_Article_161_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/bb790bc4affd/13019_2014_Article_161_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/b2c4169cf134/13019_2014_Article_161_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/0ca81deacda2/13019_2014_Article_161_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/a9f516c64753/13019_2014_Article_161_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/716fc176a97c/13019_2014_Article_161_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/f19ce3c5f33a/13019_2014_Article_161_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/5020f7c51194/13019_2014_Article_161_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/8a355e9fea9a/13019_2014_Article_161_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/c0ce67eceff9/13019_2014_Article_161_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/b272122a50b9/13019_2014_Article_161_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/a83caa48afb7/13019_2014_Article_161_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/349b1753d461/13019_2014_Article_161_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/e30e0264c1bc/13019_2014_Article_161_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/06c710ae337c/13019_2014_Article_161_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/bb790bc4affd/13019_2014_Article_161_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/572a/4190392/b2c4169cf134/13019_2014_Article_161_Fig14_HTML.jpg

相似文献

1
A new human heart vessel identification, segmentation and 3D reconstruction mechanism.一种新型人体心脏血管识别、分割及三维重建机制。
J Cardiothorac Surg. 2014 Oct 2;9:161. doi: 10.1186/s13019-014-0161-1.
2
An automated method for accurate vessel segmentation.一种用于精确血管分割的自动化方法。
Phys Med Biol. 2017 May 7;62(9):3757-3778. doi: 10.1088/1361-6560/aa6418. Epub 2017 Apr 6.
3
Automated three-dimensional vessel reconstruction based on deep segmentation and bi-plane angiographic projections.基于深度分割和双平面血管造影投影的自动三维血管重建。
Comput Med Imaging Graph. 2021 Sep;92:101956. doi: 10.1016/j.compmedimag.2021.101956. Epub 2021 Jul 21.
4
Quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images.基于磁共振图像的脊柱侧凸椎间盘 3D 重建自动分割方法的定量评估。
BMC Med Imaging. 2012 Aug 2;12:26. doi: 10.1186/1471-2342-12-26.
5
A coronary artery segmentation method based on multiscale analysis and region growing.一种基于多尺度分析和区域生长的冠状动脉分割方法。
Comput Med Imaging Graph. 2016 Mar;48:49-61. doi: 10.1016/j.compmedimag.2015.12.004. Epub 2015 Dec 21.
6
Virtual 3D IVUS vessel model for intravascular brachytherapy planning. I. 3D segmentation, reconstruction, and visualization of coronary artery architecture and orientation.用于血管内近距离放射治疗计划的虚拟3D血管内超声血管模型。I. 冠状动脉结构和方向的3D分割、重建及可视化。
Med Phys. 2003 Sep;30(9):2530-6. doi: 10.1118/1.1603964.
7
Improvement of algorithm for quantification of regional myocardial blood flow using 15O-water with PET.使用正电子发射断层扫描（PET）的15O-水对局部心肌血流量定量算法的改进。
J Nucl Med. 2004 Nov;45(11):1908-16.
8
Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net.使用双通道 U-Net 对血管内超声图像中的动脉壁进行稳健分割。
Ultrasonics. 2019 Jul;96:24-33. doi: 10.1016/j.ultras.2019.03.014. Epub 2019 Mar 23.
9
Learning tree-structured representation for 3D coronary artery segmentation.学习用于 3D 冠状动脉分割的树状结构表示。
Comput Med Imaging Graph. 2020 Mar;80:101688. doi: 10.1016/j.compmedimag.2019.101688. Epub 2019 Dec 28.
10
3D fusion of LV venous anatomy on fluoroscopy venograms with epicardial surface on SPECT myocardial perfusion images for guiding CRT LV lead placement.荧光透视静脉造影时，将左心室静脉解剖的 3D 融合与 SPECT 心肌灌注图像的心脏外膜表面融合，用于指导 CRT 左心室导线放置。
JACC Cardiovasc Imaging. 2014 Dec;7(12):1239-48. doi: 10.1016/j.jcmg.2014.09.002. Epub 2014 Nov 5.

引用本文的文献

1
Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy.运用深度学习技术在甲状腺切除术中识别喉返神经。
Sci Rep. 2021 Jul 12;11(1):14306. doi: 10.1038/s41598-021-93202-y.
2
Tissue clearing and imaging methods for cardiovascular development.用于心血管发育的组织透明化和成像方法。
iScience. 2021 Apr 1;24(4):102387. doi: 10.1016/j.isci.2021.102387. eCollection 2021 Apr 23.

本文引用的文献

1
Auto-threshold quantification of late gadolinium enhancement in patients with acute heart disease.自动门控钆延迟增强在急性心脏病患者中的定量分析。
J Magn Reson Imaging. 2013 Feb;37(2):382-90. doi: 10.1002/jmri.23814. Epub 2012 Sep 25.
2
Fast retinal vessel detection and measurement using wavelets and edge location refinement.基于小波和边缘定位细化的快速视网膜血管检测和测量。
PLoS One. 2012;7(3):e32435. doi: 10.1371/journal.pone.0032435. Epub 2012 Mar 12.
3
A review of segmentation methods in short axis cardiac MR images.短轴心脏磁共振图像分割方法综述。
Med Image Anal. 2011 Apr;15(2):169-84. doi: 10.1016/j.media.2010.12.004. Epub 2010 Dec 24.
4
A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes.三维血管管腔分割技术综述：模型、特征和提取方案。
Med Image Anal. 2009 Dec;13(6):819-45. doi: 10.1016/j.media.2009.07.011. Epub 2009 Aug 12.
5
Hybrid image segmentation using watersheds and fast region merging.使用分水岭算法和快速区域合并的混合图像分割
IEEE Trans Image Process. 1998;7(12):1684-99. doi: 10.1109/83.730380.
6
A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II.磁共振血管图像处理综述：骨架法与非骨架法：第二部分。
IEEE Trans Inf Technol Biomed. 2002 Dec;6(4):338-50. doi: 10.1109/titb.2002.804136.
7
A review on MR vascular image processing algorithms: acquisition and prefiltering: part I.磁共振血管图像处理算法综述：采集与预滤波：第一部分
IEEE Trans Inf Technol Biomed. 2002 Dec;6(4):324-37. doi: 10.1109/titb.2002.804139.
8
Three-dimensional modeling for functional analysis of cardiac images: a review.心脏图像功能分析的三维建模：综述
IEEE Trans Med Imaging. 2001 Jan;20(1):2-25. doi: 10.1109/42.906421.
9
Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images.用于医学图像中曲线结构分割与可视化的三维多尺度线滤波器
Med Image Anal. 1998 Jun;2(2):143-68. doi: 10.1016/s1361-8415(98)80009-1.