A machine learning algorithm for creating isotropic 3D aortic segmentations from routine cardiac MR localizers

Background

The identification and measurement of aortic aneurysms is an important clinical problem. While specialized high-resolution 3D CMR sequences allow detailed aortic assessment, they are time-consuming which limits their use in screening routine cardiac scans and in population studies.

Methods

A 3D U-Net, U-Net_LR, was used to create 3D isotropic segmentations of the aorta from standard anisotropic 2D trans-axial localizers with low through-plane resolution. Training data was generated from high-resolution 3D isotropic whole heart images by simulating anisotropic images that resemble the low-resolution 2D localizers (the inputs). These inputs were paired with 3D isotropic ‘ground truth’ segmentation masks (the targets) created by a clinician from the high-resolution isotropic images. Segmentation quality was evaluated using an external dataset from UK Biobank. Segmentation accuracy was measured against ground-truth segmentations from concurrently acquired cardiac-triggered, respiratory-gated, high-resolution 3D isotropic whole heart images. Finally, the proposed method was compared to U-Net_HR, a 3D U-Net variant trained directly on high-resolution 3D isotropic images. A second observer was recruited to investigate the interobserver variability.

Results

Qualitative validation on an external dataset (UK Biobank) of 180 subjects showed that 93 % of 3D segmentations with the proposed model (U-Net_LR) were considered suitable for clinical use. In quantitative analysis, the proposed method (U-Net_LR) showed good agreement with ground-truth segmentations from isotropic 3D images with a mean DICE score of 0.9, which is no difference from automated segmentations made directly on the high-resolution 3D isotropic aorta images (U-Net_HR). When comparing measurements, there is no significant difference between U-Net_LR, U-Net_HR and two clinical observers in the diameter measurements at the mid ascending aorta, mid aortic arch, and descending aorta.

Conclusions

A new method of producing isotropic 3D aortic segmentations from routine CMR 2D anisotropic localizers shows good agreement with segmentation made directly from 3D isotropic images. The method has the potential to be used as a simple screening method for aortic aneurysms without the need for additional sequences.