title: Fast segmentation of the femoral arteries from 3D MR images: A tool for rapid assessment of peripheral arterial disease.
abstract: OBJECTIVE
  The peripheral arterial disease is a powerful indicator of coexistent generalized atherosclerosis. As plaques in femoral arteries are diffused and can span a length of 30 cm, a large coverage of the arteries is required to assess the full extent of atherosclerosis. Recent development of 3D black-blood magnetic resonance imaging sequences has allowed fast acquisition of images with an extended longitudinal coverage. Vessel wall volume quantification requires the segmentation of the lumen and outer wall boundaries, and conventional manual planimetry would be too time-consuming to be feasible for analyzing images with such a large coverage. To address this challenge in image analysis, this work introduces an efficient 3D algorithm to segment the lumen and outer wall boundaries for plaque and vessel wall quantification in the femoral artery.
  
  
  METHODS
  To generate the initial lumen surface, a user identified the location of the lumen centers manually on a set of transverse images with a user-specified interslice distance (ISD). A number of geometric operators were introduced to automatically adjust the initial lumen surface based on pixel intensity and gradient along the boundary and at the center of each transverse slice. The adjusted surface was optimized by a 3D deformable model driven by the local stiffness force and external force based on image gradient. The optimized lumen surface was expanded to obtain the initial outer wall surface, which was subsequently optimized by the 3D deformable model.
  
  
  RESULTS
  The algorithm was executed with and without adjustment of the initial lumen surface and for three different selections of ISD: 10, 20, and 30 mm. The segmentation accuracy was improved in a statistically significant way with the introduction of initial lumen surface adjustment, but was insensitive to the ISD setting. When compared with the manual segmentation, the settings with adjustment have, on average, mean absolute differences (MADs) of 0.28 and 0.36 mm, respectively, for lumen and outer wall segmentations, which are significantly lower than those obtained when the adjustment operators were not applied (MAD = 0.43 and 0.59 mm for lumen and outer wall segmentations). The algorithm took about 1% of the time required for manual segmentation to complete segmenting the whole 3D femoral artery.
  
  
  CONCLUSIONS
  The proposed semiautomatic algorithm generated accurate lumen and outer wall boundaries from 3D black-blood MR images with few user interactions, thereby allowing rapid and streamlined assessment of plaque burden in the femoral arteries.
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong.
authors.forename: Weifu
authors.initials: W
authors.lastname: Chen
authors.affiliation: Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China.
authors.forename: Jianrong
authors.initials: J
authors.lastname: Xu
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong.
authors.forename: Bernard
authors.initials: B
authors.lastname: Chiu
mesh_terms: D000465:Algorithms; D005263:Femoral Artery / Q000473:pathology*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D058729:Peripheral Arterial Disease / Q000473:pathology*; D013997:Time Factors