Segmentation of Abdominal Volume Dataset Slices Guided by Single Annotated Image

One major challenge faced by segmentation techniques in analyzing and visualizing individual slices of a 3D anatomical structure, is the degree of manual interaction required. To alleviate this problem, researchers have proposed the automatic incorporation of anatomical knowledge, via medical atlases to assist with the segmentation process. Some solutions include constructing specialized simple 2D, as well as complex 3D atlases. In this paper we propose a simple method that automatically transfers a prior anatomical knowledge from a simple teaching atlas of a single 2D slice to the most similar slice in the 3D volume dataset. Segmentation of the selected anatomy is then able to be propagated automatically on the remaining slices in the dataset without further manual interaction. Our experiments are conducted on abdomen CT images and we successful delineation and visualization have been demonstrated for spleen, and kidney images.