A Novel Combined Level Set Model for Carpus Segmentation from Magnetic Resonance Images with Prior Knowledge aligned in Polar Coordinate System

Abstract

Background and ObjectiveSegmentation on carpus provides essential information for clinical applications including pathological evaluations, therapy planning, wrist biomechanical analysis, etc. Along with the acquisition procedure of magnetic resonance (MR) technique, poor quality of wrist images (e.g., occlusion, low signal-to-noise ratio, and contrast) often causes segmentation failure. MethodsIn this work, to address such problems, a shape prior enhanced level set model was proposed. By transferring a shape contour in Cartesian Coordinate System (COS) into a curve in Polar Coordinate System (POS), parameters describing conventional shape invariance, i.e., translations, rotation, and scale were simplified into a single parameter for phase shift, which strongly improved algorithm efficiency. Given a training set in COS, a confidence interval representing the corresponding curves in POS was utilized as the shape prior set term in the model. Integrated with an edge detector, a local intensity descriptor, and a regularization term, the proposed method further possessed abilities against noise, intensity inhomogeneity as well as re-initialization problem. Images from 15 in-vivo acquired MR-datasets of the human wrist were used for validation. The performance of the proposed method has been compared with three state-of-the-art methods. ResultsWe reported a Dice Similarity Coefficient of 96.88±1.20%, a Relative Volume Difference of -1.53±3.01%, a Volume Overlap Error of 6.03±2.23%, a 95% Hausdorff Distance of 1.43±0.66 mm, an Average Symmetric Surface Distance of 0.50±0.17 mm, and a Root Mean Square Distance of 0.71±0.25 mm for the proposed method. The time consumption was 36.03±19.98 s. ConclusionsExperimental results indicated that, compared with three other methods, the proposed method achieved significant improvement in terms of accuracy and efficiency.