عنوان انگلیسی مقاله:
Identification and Quantification of Cardiovascular Structures From CCTA
ترجمه فارسی عنوان مقاله:
شناسایی و تعیین ساختارهای قلبی و عروقی از CCTA
Sciencedirect - Elsevier - JACC: Cardiovascular Imaging, Corrected proof: doi:10:1016/j:jcmg:2019:08:025
Lohendran Baskaran, MBBS,a,b,c,* Gabriel Maliakal, MSC,a,* Subhi J. Al’Aref, MD,a,b Gurpreet Singh, PHD,a Zhuoran Xu, MD, MSC,a Kelly Michalak, BA,a Kristina Dolan, BA,a Umberto Gianni,a Alexander van Rosendael, MD,a Inge van den Hoogen,a Donghee Han,d Wijnand Stuijfzand,e Mohit Pandey, MSC,a Benjamin C. Lee, PHD,a Fay Lin, MD,a,b Gianluca Pontone, MD, PHD,f Paul Knaapen, MD, PHD,e Hugo Marques, MD, PHD,g Jeroen Bax, MD, PHD,h Daniel Berman, MD,d Hyuk-Jae Chang, MD, PHD,i Leslee J. Shaw, PHD,a,b James K. Min, MDa,
OBJECTIVES This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and
BACKGROUND Segmentation of cardiac structures from coronary computed tomography angiography (CCTA) images
is laborious. We designed an end-to-end deep-learning solution.
METHODS Scans were obtained from multicenter registries of 166 patients who underwent clinically indicated CCTA.
Left ventricular volume (LVV) and right ventricular volume (RVV), left atrial volume (LAV) and right atrial volume (RAV),
and left ventricular myocardial mass (LVM) were manually annotated as ground truth. A U-Netinspired, deep-learning
model was trained, validated, and tested in a 70:20:10 split.
RESULTS Mean age was 61.1 8.4 years, and 49% were women. A combined overall median Dice score of 0.9246
(interquartile range: 0.8870 to 0.9475) was achieved. The median Dice scores for LVV, RVV, LAV, RAV, and LVM were
0.938 (interquartile range: 0.887 to 0.958), 0.927 (interquartile range: 0.916 to 0.946), 0.934 (interquartile range:
0.899 to 0.950), 0.915 (interquartile range: 0.890 to 0.920), and 0.920 (interquartile range: 0.811 to 0.944),
respectively. Model prediction correlated and agreed well with manual annotation for LVV (r ¼ 0.98), RVV (r ¼ 0.97),
LAV (r ¼ 0.78), RAV (r ¼ 0.97), and LVM (r ¼ 0.94) (p < 0.05 for all). Mean difference and limits of agreement for LVV,
RVV, LAV, RAV, and LVM were 1.20 ml (95% CI: 7.12 to 9.51), 0.78 ml (95% CI: 10.08 to 8.52), 3.75 ml
(95% CI: 21.53 to 14.03), 0.97 ml (95% CI: 6.14 to 8.09), and 6.41 g (95% CI: 8.71 to 21.52), respectively.
CONCLUSIONS A deep-learning model rapidly segmented and quantified cardiac structures. This was done with
high accuracy on a pixel level, with good agreement with manual annotation, facilitating its expansion into areas of research
and clinical import. (J Am Coll Cardiol Img 2019;-:-–-) © 2019 by the American College of Cardiology Foundation.