Mice endplate segmentation from micro-CT data through graph-based trabecula recognition

Though segmentation of spinal column from medical images have been intensively studied for decade, most of the works were concentrated on the segmentation of the vertebral body and arch, instead of the endplate. Recently, the increasing study on degeneration analysis of vertebra and intervertebral disc (IVD) make endplate segmentation as important as others. While the accurately segmentation of mice endplate from micro computed tomography (CT) images is challenging. The major difficulties include potential high system payload and poor run-time efficiency resulting from high-resolution micro-CT data, highly complicated and variable shape of the vertebra tissues, and the ambiguous segmentation boundary due to the similarity of spongy structures inside both the endplate and its adjacent vertebral body. To solve the problems, the core idea of the proposed method is to identify trabeculae between the endplate and the body through a graph-based strategy. In addition, in order to reduce the data complexity, an endplate-targeted region of interest (ROI) extraction method is introduced according to the analysis of spatial relationship and variety of bone density of vertebra. Furthermore, shape priori of endplate in both two-dimensional and three-dimensional are extracted to assist in the segmentation. Finally, an iterative cutting procedure is implemented to produce the final result. Experiments were carried out which validate the performances of the proposed method in terms of effectiveness and accuracy.