A Novel Combined Level Set Model for Carpus Segmentation from Magnetic Resonance Images with Prior Knowledge aligned in Polar Coordinate System. (September 2021) - Li, Jianzhang; Nebelung, Sven; Schock, Justus; Rath, Björn; Tingart, Markus; Liu, Yu; Siroros, Nad; Eschweiler, Jörg
Highlights: A novel concept for high efficient shape alignment was proposed by transferring shapes in Cartesian coordinate system into polar coordinate system. Various energy terms were integrated to handle poor image quality in magnetic resonance wrist images, e.g., occlusion, blurry boundary, noises, and intensity inhomogeneity. The proposed approach was validated on 15 in-vivo acquired wrist magnetic resonance imaging datasets. The results indicated a superior performance on accuracy and efficiency over the comparisons. Abstract: Background and Objective: Segmentation 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. Methods: In 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. Results: We 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. Conclusions: Experimental results indicated that, compared with three other methods, the proposed method achieved significant improvement in terms of accuracy and efficiency.