Anterior Cruciate Ligament Segmentation from Knee MR Images Using Graph Cuts with Geometric and Probabilistic Shape Constraints

Automatic segmentation of anterior cruciate ligament (ACL) is a challenging task due to its similar intensities with adjacent soft tissues, and inhomogeneity inside it in 3D knee magnetic resonance (MR) images. In this paper, an automatic ACL segmentation from 3D knee MR images using graph cuts is proposed. The proposed method consists of two steps: First, in the rough segmentation, adaptive thresholding using GMM fitting and ACL candidates extraction is performed to extract initial object and background candidates. Second, in the fine segmentation, iterative graph cut segmentation is incorporated with additional constraints including geometric and probabilistic shape costs to prevent the segmented ACL label from the leakage into adjacent soft tissues e.g. posterior cruciate ligament (PCL) and cartilage. In the experimental results, compared to the preceding work [1], the proposed method shows overall improved performances in sensitivity, specificity, and Dice similarity coefficient of 25%, 0.1%, and 29% for whole ACL, 34%, 0.5%, and 41% for major stem of ACL, respectively.