Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning

PLoS One. 2020 May 6;15(5):e0232573. doi: 10.1371/journal.pone.0232573. eCollection 2020.

Abstract

Objectives: To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.

Background: Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images.

Methods: Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split.

Results: The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups.

Conclusions: An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Computed Tomography Angiography / methods*
  • Coronary Vessels / diagnostic imaging*
  • Deep Learning*
  • Female
  • Heart / diagnostic imaging*
  • Heart Atria / diagnostic imaging
  • Heart Ventricles / diagnostic imaging
  • Humans
  • Male
  • Middle Aged

Associated data

  • Dryad/10.5061/dryad.9s4mw6mc9

Grants and funding

The research reported in this manuscript was supported by the Dalio Institute of Cardiovascular Imaging (New York, NY, USA). James K. Min received funding from the Dalio Foundation, National Institutes of Health, and GE Healthcare. Dr. Min serves on the scientific advisory board of Arineta and GE Healthcare, and became founder and an employee of Cleerly, Inc after this research was conducted. Dr. Jonathon Leipsic is a consultant and has reported stock options with Circle CVI and HeartFlow. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.