Level set framework of multi-atlas label fusion with applications to magnetic resonance imaging segmentation of brain region of interests and cardiac left ventricles

Background and Objectives: This paper evaluates the performance of a variational level set method for performing label fusion through the use of a penalty term, label fusion term, and length regularization term, which automatically labels objects of interest in biomedical images. This paper is an extension of our preliminary work in the conference paper. We mainly focus on the validation of the variational level set method. Subjects and Methods: Label fusion is achieved by combining the three terms: label fusion term, image data term, and regularization term. The curve evolution derived from the energy minimization is impacted by the three terms simultaneously to achieve optimal label fusion. Each label obtained from the nonlinear registration method is represented by a level set function whose zero level contour encloses the labeled region. In Lu et al .’s paper, they employ the level set formulation only for hippocampus segmentation. Results: Our method is compared with majority voting (MV), local weighted voting (LWV), and Simultaneous Truth and Performance Level Estimation (STAPLE). The method is evaluated on MICCAI 2012 Multi-Atlas Labeling challenge and MICCAI 2012 ventricle segmentation challenge. The mean Dice metric is computed using different atlases and produces results with 0.85 for the hippocampus, 0.77 for the amygdala, 0.87 for the caudate, 0.78 for the pallidum, 0.89 for the putamen, 0.91 for the thalamus, and 0.78 for cardiac left ventricles. Conclusions: Experimental results demonstrate that our method is robust to parameter setting and outperforms MV, LWV, and STAPLE. The image data term plays a key role in improving the segmentation accuracy. Our method can obtain satisfactory results with fewer atlases.