Identification and Quantification of Cardiovascular Structures From CCTA: An End-to-End, Rapid, Pixel-Wise, Deep-Learning Method

Lohendran Baskaran, Gabriel Maliakal, Subhi J. Al'Aref, Gurpreet Singh, Zhuoran Xu, Kelly Michalak, Kristina Dolan, Umberto Gianni, Alexander van Rosendael, Inge van den Hoogen, Donghee Han, Wijnand Stuijfzand, Mohit Pandey, Benjamin C. Lee, Fay Lin, Gianluca Pontone, Paul Knaapen, Hugo Marques, Jeroen Bax, Daniel BermanHyuk Jae Chang, Leslee J. Shaw, James K. Min

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Objectives: This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification. 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-Net−inspired, 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 = 0.98), RVV (r = 0.97), LAV (r = 0.78), RAV (r = 0.97), and LVM (r = 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.

Original languageEnglish
Pages (from-to)1163-1171
Number of pages9
JournalJACC: Cardiovascular Imaging
Volume13
Issue number5
DOIs
Publication statusPublished - 2020 May

Bibliographical note

Funding Information:
The study was supported by the Dalio Institute of Cardiovascular Imaging. Dr. Lee has been a consultant for Cleerly. Dr. Bax has received speaker fees from Abbott Vascular and Boehringer Ingelheim. Dr. Min has received funding from the Dalio Foundation, National Institutes of Health, and GE Healthcare; has served on the scientific advisory board of Arineta and GE Healthcare; and has an equity interest in Cleerly. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Joseph Schoepf, MD, served as Guest Editor for this paper.

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

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