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

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

Automated summary

This study aimed to develop and evaluate a deep learning-based segmentation method for automatically segmenting the cardiac chambers and great thoracic vessels from 4D flow MRI. The results demonstrated that the deep learning-based method achieved good segmentation accuracy.