Automatic segmentation of peripheral arteries and veins in ferumoxytol‐enhanced MR angiography. Issue 2 (5th October 2021)

• Record Type: Journal Article
• Authors: Ghodrati, Vahid; Rivenson, Yair; Prosper, Ashley; de Haan, Kevin; Ali, Fadil; Yoshida, Takegawa; Bedayat, Arash; Nguyen, Kim‐Lien; Finn, J. Paul; Hu, Peng
• Is Part Of: Magnetic resonance in medicine. Volume 87:Issue 2(2022)
• Page Start: 984
• Page End: 998
• Publication Date: 2021-10-05
• Subjects: blood vessel segmentation -- contrast enhancement magnetic resonance angiography -- convolutional neural networks -- deep learning -- ferumoxytol‐enhanced MRI -- peripheral vessel segmentation; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.29026
• Languages: English
• ISSNs: 0740-3194

Abstract

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.