Automatic segmentation of peripheral arteries and veins in ferumoxytol‐enhanced MR angiography

Abstract

Purpose To automate the segmentation of the peripheral arteries and veins in the lower extremities based on ferumoxytol‐enhanced MR angiography (FE‐MRA). Methods Our automated pipeline has 2 sequential stages. In the first stage, we used a 3D U‐Net with local attention gates, which was trained based on a combination of the Focal Tversky loss with region mutual loss under a deep supervision mechanism to segment the vasculature from the high‐resolution FE‐MRA datasets. In the second stage, we used time‐resolved images to separate the arteries from the veins. Because the ultimate segmentation quality of the arteries and veins relies on the performance of the first stage, we thoroughly evaluated the different aspects of the segmentation network and compared its performance in blood vessel segmentation with currently accepted state‐of‐the‐art networks, including Volumetric‐Net, DeepVesselNet‐FCN, and Uception. Results We achieved a competitive F1 = 0.8087 and recall = 0.8410 for blood vessel segmentation compared with F1 = (0.7604, 0.7573, 0.7651) and recall = (0.7791, 0.7570, 0.7774) obtained with Volumetric‐Net, DeepVesselNet‐FCN, and Uception. For the artery and vein separation stage, we achieved F1 = (0.8274/0.7863) in the calf region, which is the most challenging region in peripheral arteries and veins segmentation. Conclusion Our pipeline is capable of fully automatic vessel segmentation based on FE‐MRA without need for human interaction in <4 min. This method improves upon manual segmentation by radiologists, which routinely takes several hours.

Unveiling the next generation of MRI contrast agents: current insights and perspectives on ferumoxytol-enhanced MRI

“…A concern in peripheral vascular imaging is the overlapping of arteries and veins, which might obscure observations. Nonetheless, with high spatial resolutions, distinguishing between arteries and veins becomes feasible through post-imaging processing [ 41 ].…”

“…Ghodrati et al. developed a unique two-stage deep-learning approach to achieve precise segmentation of peripheral arteries and veins in FE-MRA [ 41 ] (Fig. 8 ).…”

“…Reproduced with permission from ref. [ 41 ]. Copyright 2021, International Society for Magnetic Resonance in Medicine.…”