Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning

Abstract

Background: Segmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable motion and limited contrast between blood and tissue.

Purpose: To develop and evaluate a deep learning-based segmentation method to automatically segment the cardiac chambers and great thoracic vessels from 4D flow MRI.

Study type: Retrospective.

Subjects: A total of 205 subjects, including 40 healthy volunteers and 165 patients with a variety of cardiac disorders were included. Data were randomly divided into training (n = 144), validation (n = 20), and testing (n = 41) sets.

Field strength/sequence: A 3 T/time-resolved velocity encoded 3D gradient echo sequence (4D flow MRI).

Assessment: A 3D neural network based on the U-net architecture was trained to segment the four cardiac chambers, aorta, and pulmonary artery. The segmentations generated were compared to manually corrected atlas-based segmentations. End-diastolic (ED) and end-systolic (ES) volumes of the four cardiac chambers were calculated for both segmentations.

Statistical tests: Dice score, Hausdorff distance, average surface distance, sensitivity, precision, and miss rate were used to measure segmentation accuracy. Bland-Altman analysis was used to evaluate agreement between volumetric parameters.

Results: The following evaluation metrics were computed: mean Dice score (0.908 ± 0.023) (mean ± SD), Hausdorff distance (1.253 ± 0.293 mm), average surface distance (0.466 ± 0.136 mm), sensitivity (0.907 ± 0.032), precision (0.913 ± 0.028), and miss rate (0.093 ± 0.032). Bland-Altman analyses showed good agreement between volumetric parameters for all chambers. Limits of agreement as percentage of mean chamber volume (LoA%), left ventricular: 9.3%, 13.5%, left atrial: 12.4%, 16.9%, right ventricular: 9.9%, 15.6%, and right atrial: 18.7%, 14.4%; for ED and ES, respectively.

Data conclusion: The addition of this technique to the 4D flow MRI assessment pipeline could expedite and improve the utility of this type of acquisition in the clinical setting.

Evidence level: 4 TECHNICAL EFFICACY: Stage 1.

Keywords: 4D flow MRI; cardiovascular MRI; convolutional neural networks; deep learning; segmentation.

FIGURE 1

Results obtained in the best, median, and worst cases according to Dice scores visualized as isosurface renderings. Dice scores of 0.96, 0.91, and 0.86, respectively. Each case includes a comparison between the ground truth segmentations and those generated by the CNN at end‐diastole and end‐systole. Yellow: Left ventricle, orange: Left atrium, dark blue: Right ventricle, light blue: Right atrium, red: Aorta, green: Pulmonary artery.

FIGURE 2

Results obtained in the best, median, and worst cases according to Dice scores. Dice scores of 0.96, 0.91, and 0.86, respectively. The segmentations have been superimposed over a four‐chamber image of the heart. Each case includes a comparison between the ground truth segmentations and those generated by the CNN at end‐diastole and end‐systole. Yellow: Left ventricle, orange: Left atrium, dark blue: Right ventricle, light blue: Right atrium, red: Aorta.

FIGURE 3

Flow streamlines generated at a mid‐systolic timeframe using the ground truth and CNN results for the best, median, and worst Dice scores. Dice scores of 0.96, 0.91, and 0.86, respectively. Pulmonary flows are depicted using shades of blue, and systemic flows using shades of red. Streamlines were generated independently for each region included in the segmentation.

FIGURE 4

Metric results on the test dataset for each region included in the segmentations. LV = left ventricle; LA = left atrium; RV = right ventricle; RA = right atrium; Ao = aorta; PA = pulmonary artery.

FIGURE 5

Dice scores calculated per timeframe on the test set. The topmost plot combines all regions. LV = left ventricle; LA = left atrium; RV = right ventricle; RA = right atrium; Ao = aorta; PA = pulmonary artery.

FIGURE 6

Bland–Altman plots of end‐diastolic volume (EDV) and end‐systolic volume (ESV) for the cardiac chambers. The dashed blue line shows the mean difference, while the dashed red lines denote the 95% limits of agreement (± 1.96 * standard deviation). GT = ground truth; LV = left ventricle, LA = left atrium; RV = right ventricle; RA = right atrium;EDV = end‐diastolic volume; ESV = end‐systolic volume.

FIGURE 7

Bland–Altman plots of the total kinetic energy over the cardiac cycle, for all regions. The dashed blue line shows the mean difference, while the dashed red lines denote the 95% limits of agreement (± 1.96 * standard deviation). GT = ground truth; LV = left ventricle; LA = left atrium; RV = right ventricle; RA = right atrium; Ao = aorta; PA = pulmonary artery; tot KE: total kinetic energy.