Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms. (December 2016) - Klepaczko, Artur; Szczypiński, Piotr; Deistung, Andreas; Reichenbach, Jürgen R.; Materka, Andrzej
Highlights: Introduction of a framework for quantitative and observer-independent validation of vessel segmentation in the MRA images. Application of a custom MRA simulator as a tool for quantitative validation of vessel segmentation algorithms. Development of a realistic digital phantom of an intracranial arterial tree based on a real Time-of-Flight data set. Performing thorough validation of a level-set vessel segmentation algorithm implemented in the Vascular Modeling Toolkit. A comparison study of three vessel segmentation algorithms based on level-set and multi-scale vessel enhancement functions. Abstract: Background and objective: Accurate vessel segmentation of magnetic resonance angiography (MRA) images is essential for computer-aided diagnosis of cerebrovascular diseases such as stenosis or aneurysm. The ability of a segmentation algorithm to correctly reproduce the geometry of the arterial system should be expressed quantitatively and observer-independently to ensure objectivism of the evaluation. Methods: This paper introduces a methodology for validating vessel segmentation algorithms using a custom-designed MRA simulation framework. For this purpose, a realistic reference model of an intracranial arterial tree was developed based on a real Time-of-Flight (TOF) MRA data set. With this specific geometry blood flow was simulated and a series of TOF images was synthesized using various acquisition protocol parameters and signal-to-noise ratios. The synthesized arterial tree was then reconstructed using a level-set segmentation algorithm available in the Vascular Modeling Toolkit (VMTK). Moreover, to present versatile application of the proposed methodology, validation was also performed for two alternative techniques: a multi-scale vessel enhancement filter and the Chan–Vese variant of the level-set-based approach, as implemented in the Insight Segmentation and Registration Toolkit (ITK). The segmentation results were compared against the reference model. Results: The accuracy in determining the vessels centerline courses was very high for each tested segmentation algorithm (mean error rate = 5.6% if using VMTK). However, the estimated radii exhibited deviations from ground truth values with mean error rates ranging from 7% up to 79%, depending on the vessel size, image acquisition and segmentation method. Conclusions: We demonstrated the practical application of the designed MRA simulator as a reliable tool for quantitative validation of MRA image processing algorithms that provides objective, reproducible results and is observer independent.