Fully automated and robust analysis technique for popliteal artery vessel wall evaluation (FRAPPE) using neural network models from standardized knee MRI

  • Li Chen
    Department of Electrical and Computer Engineering University of Washington Seattle Washington USA
  • Gador Canton
    Department of Radiology University of Washington Seattle Washington USA
  • Wenjin Liu
    Department of Radiology University of Washington Seattle Washington USA
  • Daniel S. Hippe
    Department of Radiology University of Washington Seattle Washington USA
  • Niranjan Balu
    Department of Radiology University of Washington Seattle Washington USA
  • Hiroko Watase
    Department of Radiology University of Washington Seattle Washington USA
  • Thomas S. Hatsukami
    Department of Surgery University of Washington Seattle Washington USA
  • John C. Waterton
    Centre for Imaging Sciences, Manchester Academic Health Science Centre The University of Manchester Manchester United Kingdom
  • Jenq‐Neng Hwang
    Department of Electrical and Computer Engineering University of Washington Seattle Washington USA
  • Chun Yuan
    Department of Radiology University of Washington Seattle Washington USA

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<jats:sec><jats:title>Purpose</jats:title><jats:p>To develop a fully automated vessel wall (VW) analysis workflow (fully automated and robust analysis technique for popliteal artery evaluation, FRAPPE) on the popliteal artery in standardized knee MR images.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Popliteal artery locations were detected from each MR slice by a deep neural network model and connected into a 3D artery centerline. Vessel wall regions around the centerline were then segmented using another neural network model for segmentation in polar coordinate system. Contours from vessel wall segmentations were used for vascular feature calculation, such as mean wall thickness and wall area. A transfer learning and active learning framework was applied in training the localization and segmentation neural network models to maintain accuracy while reducing manual annotations. This new popliteal artery analysis technique (FRAPPE) was validated against manual segmentation qualitatively and quantitatively in a series of 225 cases from the Osteoarthritis Initiative (OAI) dataset.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>FRAPPE demonstrated high accuracy and robustness in locating popliteal arteries, segmenting artery walls, and quantifying arterial features. Qualitative evaluations showed 1.2% of slices had noticeable major errors, including segmenting the wrong target and irregular vessel wall contours. The mean Dice similarity coefficient with manual segmentation was 0.79, which is comparable to inter‐rater variations. Repeatability evaluations show most of the vascular features have good to excellent repeatability from repeated scans of same subjects, with intra‐class coefficient ranging from 0.80 to 0.98.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>This technique can be used in large population‐based studies, such as OAI, to efficiently assess the burden of atherosclerosis from routine MR knee scans.</jats:p></jats:sec>

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