Deep Learning-based Automated Aortic Area and Distensibility Assessment: the Multi-Ethnic Study of Atherosclerosis (MESA).
This study details application of deep learning for automatic segmentation of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging for automatic aortic analysis on the large MESA cohort with assessment on an external cohort of thoracic aortic aneurysm (TAA) patie...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 3; pp. 594 - 605 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157184667&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157184667 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2022 vid: 35 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157184667 155486634 157184667 157184667 10.1007/s10278-021-00529-z 157184667 ppf: 594 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning-based Automated Aortic Area and Distensibility Assessment: the Multi-Ethnic Study of Atherosclerosis (MESA). aug: au: Jani, Vivek P. Kachenoura, Nadjia Redheuil, Alban Teixido-Tura, Gisela Bouaou, Kevin Bollache, Emilie Mousseaux, Elie De Cesare, Alain Kutty, Shelby Wu, Colin O. Bluemke, David A. Lima, Joao A. C. Ambale-Venkatesh, Bharath affil: Department of Radiology, Johns Hopkins University, 600 North Wolfe St, 21287, Baltimore, MD, USA sug: subj: Deep Learning Utilization Aorta Pathology Aorta, Thoracic Radiography Magnetic Resonance Imaging Methods Atherosclerosis Aortic Aneurysm, Thoracic Neural Networks (Computer) Human Female Male Prospective Studies Cardiovascular Diseases Image Processing, Computer Assisted Validity Outcome Assessment Correlation Coefficient Descriptive Statistics Confidence Intervals Female Male ab: This study details application of deep learning for automatic segmentation of the ascending and descending aorta from 2D phase-contrast cine magnetic resonance imaging for automatic aortic analysis on the large MESA cohort with assessment on an external cohort of thoracic aortic aneurysm (TAA) patients. This study includes images and corresponding analysis of the ascending and descending aorta at the pulmonary artery bifurcation from the MESA study. Train, validation, and internal test sets consisted of 1123 studies (24,282 images), 374 studies (8067 images), and 375 studies (8069 images), respectively. The external test set of TAAs consisted of 37 studies (3224 images). CNN performance was evaluated utilizing a dice coefficient and concordance correlation coefficients (CCC) of geometric parameters. Dice coefficients were as high as 97.55% (CI: 97.47–97.62%) and 93.56% (CI: 84.63–96.68%) on the internal and external test of TAAs, respectively. CCC for maximum and minimum and ascending aortic area were 0.969 and 0.950, respectively, on the internal test set and 0.997 and 0.995, respectively, for the external test. The absolute differences between manual and deep learning segmentations for ascending and descending aortic distensibility were 0.0194 × 10−4 ± 9.67 × 10−4 and 0.002 ± 0.001 mmHg−1, respectively, on the internal test set and 0.44 × 10−4 ± 20.4 × 10−4 and 0.002 ± 0.001 mmHg−1, respectively, on the external test set. We successfully developed a U-Net-based aortic segmentation and analysis algorithm in both MESA and in external cases of TAA. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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