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Ghodrati V, Rivenson Y, Prosper A, de Haan K, Ali F, Yoshida T, Bedayat A, Nguyen KL, Finn JP, Hu P. Automatic segmentation of peripheral arteries and veins in ferumoxytol-enhanced MR angiography. Magn Reson Med 2021; 87:984-998. [PMID: 34611937 DOI: 10.1002/mrm.29026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 09/03/2021] [Accepted: 09/09/2021] [Indexed: 11/10/2022]
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.
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Affiliation(s)
- Vahid Ghodrati
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, California, USA
| | - Yair Rivenson
- Electrical and Computer Engineering Department, University of California, Los Angeles, California, USA
| | - Ashley Prosper
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Kevin de Haan
- Electrical and Computer Engineering Department, University of California, Los Angeles, California, USA
| | - Fadil Ali
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, California, USA
| | - Takegawa Yoshida
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Arash Bedayat
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Kim-Lien Nguyen
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Department of Medicine (Cardiology), David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - J Paul Finn
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Peng Hu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, California, USA
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