@article{M8D7D1E58,
title = "Anterior Cruciate Ligament Segmentation in Knee MRI with Locally-aligned Probabilistic Atlas and Iterative Graph Cuts",
journal = "Journal of KIISE, JOK",
year = "2015",
issn = "2383-630X",
doi = "",
author = "Han Sang Lee,Helen Hong",
keywords = "knee MRI,anterior cruciate ligament,image segmentation,multi-atlas segmentation",
abstract = "Segmentation of the anterior cruciate ligament (ACL) in knee MRI remains a challenging task due to its inhomogeneous signal intensity and low contrast with surrounding soft tissues. In this paper, we propose a multi-atlas-based segmentation of the ACL in knee MRI with locally-aligned probabilistic atlas (PA) in an iterative graph cuts framework. First, a novel PA generation method is proposed with global and local multi-atlas alignment by means of rigid registration. Second, with the generated PA, segmentation of the ACL is performed by maximum-aposteriori (MAP) estimation and then by graph cuts. Third, refinement of ACL segmentation is performed by improving shape prior through mask-based PA generation and iterative graph cuts. Experiments were performed with a Dice similarity coefficients of 75.0%, an average surface distance of 1.7 pixels, and a root mean squared distance of 2.7 pixels, which increased accuracy by 12.8%, 22.7%, and 22.9%, respectively, from the graph cuts with patient-specific shape constraints."
}