title: Joint segmentation of lumen and outer wall from femoral artery MR images: Towards 3D imaging measurements of peripheral arterial disease.
abstract: Three-dimensional (3D) measurements of peripheral arterial disease (PAD) plaque burden extracted from fast black-blood magnetic resonance (MR) images have shown to be more predictive of clinical outcomes than PAD stenosis measurements. To this end, accurate segmentation of the femoral artery lumen and outer wall is required for generating volumetric measurements of PAD plaque burden. Here, we propose a semi-automated algorithm to jointly segment the femoral artery lumen and outer wall surfaces from 3D black-blood MR images, which are reoriented and reconstructed along the medial axis of the femoral artery to obtain improved spatial coherence between slices of the long, thin femoral artery and to reduce computation time. The developed segmentation algorithm enforces two priors in a global optimization manner: the spatial consistency between the adjacent 2D slices and the anatomical region order between the femoral artery lumen and outer wall surfaces. The formulated combinatorial optimization problem for segmentation is solved globally and exactly by means of convex relaxation using a coupled continuous max-flow (CCMF) model, which is a dual formulation to the convex relaxed optimization problem. In addition, the CCMF model directly derives an efficient duality-based algorithm based on the modern multiplier augmented optimization scheme, which has been implemented on a GPU for fast computation. The computed segmentations from the developed algorithm were compared to manual delineations from experts using 20 black-blood MR images. The developed algorithm yielded both high accuracy (Dice similarity coefficients ≥ 87% for both the lumen and outer wall surfaces) and high reproducibility (intra-class correlation coefficient of 0.95 for generating vessel wall area), while outperforming the state-of-the-art method in terms of computational time by a factor of ≈ 20.
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada. Electronic address: eukwatt1@jhu.edu.
authors.forename: Eranga
authors.initials: E
authors.lastname: Ukwatta
authors.affiliation: Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada.
authors.forename: Jing
authors.initials: J
authors.lastname: Yuan
authors.affiliation: Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada.
authors.forename: Wu
authors.initials: W
authors.lastname: Qiu
authors.affiliation: Department of Computing, Imperial College London, London, UK.
authors.forename: Martin
authors.initials: M
authors.lastname: Rajchl
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong.
authors.forename: Bernard
authors.initials: B
authors.lastname: Chiu
authors.affiliation: Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada.
authors.forename: Aaron
authors.initials: A
authors.lastname: Fenster
mesh_terms: D000465:Algorithms; D005263:Femoral Artery / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D058729:Peripheral Arterial Disease / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Convex relaxation; Coupled continuous max-flow; Multi-region segmentation; Peripheral arterial disease; Spatial-consistency prior