Joint segmentation of lumen and outer wall from femoral artery MR images: Towards 3D imaging measurements of peripheral arterial disease

Autor: Wu Qiu, Aaron Fenster, Jing Yuan, Eranga Ukwatta, Bernard Chiu, Martin Rajchl
Rok vydání: 2015
Předmět:
medicine.medical_specialty
Optimization problem
Computation
Health Informatics
02 engineering and technology
Femoral artery
Sensitivity and Specificity
Magnetic resonance angiography
Pattern Recognition
Automated
030218 nuclear medicine & medical imaging
Peripheral Arterial Disease
03 medical and health sciences
Imaging
Three-Dimensional
0302 clinical medicine
Medial axis
medicine.artery
Image Interpretation
Computer-Assisted
0202 electrical engineering
electronic engineering
information engineering
medicine
Humans
Radiology
Nuclear Medicine and imaging
Segmentation
Global optimization
Mathematics
Radiological and Ultrasound Technology
medicine.diagnostic_test
Reproducibility of Results
Image Enhancement
Computer Graphics and Computer-Aided Design
Femoral Artery
Subtraction Technique
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Radiology
Algorithms
Magnetic Resonance Angiography
Biomedical engineering
Lumen (unit)
Zdroj: Medical Image Analysis. 26:120-132
ISSN: 1361-8415
DOI: 10.1016/j.media.2015.08.004
Popis: 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.
Databáze: OpenAIRE
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