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高度自动化软件在血管内光学相干断层成像中的综合支架分析中的应用与评估。

Application and Evaluation of Highly Automated Software for Comprehensive Stent Analysis in Intravascular Optical Coherence Tomography.

机构信息

Microsoft, Azure Global, Cambridge, MA, 02142, USA.

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.

出版信息

Sci Rep. 2020 Feb 7;10(1):2150. doi: 10.1038/s41598-020-59212-y.

DOI:10.1038/s41598-020-59212-y
PMID:32034252
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7005885/
Abstract

Intravascular optical coherence tomography (IVOCT) is used to assess stent tissue coverage and malapposition in stent evaluation trials. We developed the OCT Image Visualization and Analysis Toolkit for Stent (OCTivat-Stent), for highly automated analysis of IVOCT pullbacks. Algorithms automatically detected the guidewire, lumen boundary, and stent struts; determined the presence of tissue coverage for each strut; and estimated the stent contour for comparison of stent and lumen area. Strut-level tissue thickness, tissue coverage area, and malapposition area were automatically quantified. The software was used to analyze 292 stent pullbacks. The concordance-correlation-coefficients of automatically measured stent and lumen areas and independent manual measurements were 0.97 and 0.99, respectively. Eleven percent of struts were missed by the software and some artifacts were miscalled as struts giving 1% false-positive strut detection. Eighty-two percent of uncovered struts and 99% of covered struts were labeled correctly, as compared to manual analysis. Using the highly automated software, analysis was harmonized, leading to a reduction of inter-observer variability by 30%. With software assistance, analysis time for a full stent analysis was reduced to less than 30 minutes. Application of this software to stent evaluation trials should enable faster, more reliable analysis with improved statistical power for comparing designs.

摘要

血管内光学相干断层扫描(IVOCT)用于评估支架组织覆盖和支架贴壁不良的支架评估试验。我们开发了用于支架的 OCT 图像可视化和分析工具包(OCTivat-Stent),用于 IVOCT 拉回的高度自动化分析。算法自动检测导丝、管腔边界和支架支柱;确定每个支柱的组织覆盖情况;并估计支架轮廓,以比较支架和管腔面积。支架水平的组织厚度、组织覆盖面积和贴壁不良面积被自动量化。该软件用于分析 292 个支架拉回。自动测量的支架和管腔面积与独立手动测量的一致性相关系数分别为 0.97 和 0.99。软件漏检了 11%的支架,一些伪影被误判为支架,导致 1%的假阳性支架检测。与手动分析相比,82%的未覆盖支架和 99%的覆盖支架被正确标记。使用高度自动化的软件,分析得到了协调,观察者间的变异性减少了 30%。有了软件的辅助,完整支架分析的分析时间减少到不到 30 分钟。该软件在支架评估试验中的应用应能实现更快、更可靠的分析,并提高比较设计的统计能力。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a0e/7005885/72be32289701/41598_2020_59212_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a0e/7005885/fa839e4a7730/41598_2020_59212_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a0e/7005885/cad4e93960fe/41598_2020_59212_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a0e/7005885/f7cc6780ebe8/41598_2020_59212_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a0e/7005885/33292e11120c/41598_2020_59212_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a0e/7005885/765a26fed577/41598_2020_59212_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a0e/7005885/acd5abcbe1af/41598_2020_59212_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a0e/7005885/9adb1fd625c8/41598_2020_59212_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a0e/7005885/a1b3c656bfe3/41598_2020_59212_Fig12_HTML.jpg

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