Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model

Author:

Krebs Jonathan R.ORCID,Imran Muhammad,Fazzone Brian,Viscardi Chelsea,Berwick Benjamin,Stinson Griffin,Heithaus Evans,Upchurch Gilbert R.,Shao Wei,Cooper Michol A.

Abstract

IntroductionMachine learning techniques have shown excellent performance in 3D medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) utilizing SVS/STS-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between auTBAD patients based on rate of aortic growth.MethodsPatients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two CT scans were analyzed: (1) the baseline CTA at index admission, and (2) either the most recent surveillance CTA, or the most recent CTA prior to an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase ≥5mm/year) and no/slow growth (diameter increase <5mm/year).Deidentified images were imported into an open-source software package for medical image analysis and randomly partitioned into training(80%), validation(10%), and testing(10%) cohorts. Training datasets were manually segmented based on SVS/STS criteria. A custom segmentation framework was used to generate the predicted segmentation output and aortic zone volumes.ResultsOf 59 patients identified for inclusion, rapid growth was observed in 33 (56%) patients and no/slow growth was observed in 26 (44%) patients. There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (p>0.05 for all). Median duration between baseline and interval CT was 1.07 years (IQR 0.38-2.57). Post-discharge aortic intervention was performed in 13 (22%) of patients at a mean of 1.5±1.2 years, with no difference between groups (p>0.05). In both groups, all zones of the thoracic and abdominal aorta increased in volume over time, with the largest relative increase in Zone 5 with a median 24% increase (IQR 4.4-37). Baseline zone 3 volumes were larger in the no/slow growth (6v3) than the rapid growth group (5v3) (p=0.03). There were no other differences in baseline zone volumes between groups (p>0.05 for all). Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in Zones 4 (0.82), 5(0.88), and 9(0.91).ConclusionsTo our knowledge this is the first description of an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open-source, trained model demonstrates high concordance to the manually segmented aortas with the strongest performance in zones 4, 5, and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between rapid growth and no/slow growth patients.ARTICLE HIGHLIGHTSType of ResearchSingle-center retrospective cohort studyKey FindingsA deep learning model was developed to analyze volumetric growth in patients with medically managed acute uncomplicated type B aortic dissection. Volumetric growth was most pronounced in zones 5 (24%), 4 (13%), and 3 (11%). Model performance was best in zones 4, 5, and 9.Take Home MessageA trained, automated, open-source aortic zone segmentation model can accurately track changes in aortic growth by zone over time, providing framework for further clinical applications.Table of Contents SummaryA trained, automated model was developed to analyze aortic zone volumetric growth in a retrospective study of 59 patients with medically managed acute uncomplicated TBAD. Volumetric growth was most pronounced in zones 3-5, while model performance was best in zones 4,5, and 9.

Publisher

Cold Spring Harbor Laboratory

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3