Automatic delineation of anterior and posterior cruciate ligaments by combining deep learning and deformable atlas based segmentation


Quantitative evaluation of bones and ligaments around knee joint from magnetic resonance imaging (MRI) often requires the boundaries of selected structures to be manually traced using computer software. It may take several hours to delineate all structures of interest in a three-dimensional (3D) dataset used for the evaluation. Thus, providing automated tools, which can delineate knee anatomical structures can improve productivity and efficiency in radiology departments. In recent years, 3D deep convolutional neural networks (3D CNN) have been successfully used for segmentation of knee bones and cartilage. However, the key challenge is segmentation of the anterior cruciate ligament (ACL) and the posterior cruciate ligament (PCL), due to high variability of intensities in the areas of pathologies such as ligament tear. In this approach, an open source 3D CNN is adapted for segmentation of knee bones and ligaments in the knee MRI. The segmentation accuracy of ACL and PCL is improved further by atlas based segmentation technique. The atlas mask is non-rigidly aligned with the patient image based on composite of rigid and deformable vector field derived between the bone masks in the atlas and corresponding segmented bone masks in the patient image. The level set functions corresponding to particular objects of interest of the deformed atlas are used to refine segmentation of the corresponding objects in the patient image. The accuracy of the proposed method is assessed using Dice coefficient score for 50 manual segmentations of bone, cartilage and ligaments comprising of both normal and knee injury cases. Our results show that the proposed approach offers a viable alternative to manual contouring of knee MRI volume by a human reader with improved accuracy compared to the 3D CNN.

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