A Feasibility Study of Automatic Multi-Organ Segmentation Using Probabilistic Atlas

Thoracic and abdominal multi-organ segmentation has been a challenging problem due to the inter-subject variance of human thoraxes and abdomens as well as the complex 3D intra-subject variance among organs. In this paper, we present a preliminary method for automatically segmenting multiple organs using non-enhanced CT data. The method is based on a simple framework using generic tools and requires no organ-specific prior knowledge. Specifically, we constructed a grayscale CT volume along with a probabilistic atlas consisting of six thoracic and abdominal organs: lungs (left and right), liver, kidneys (left and right) and spleen. A non-rigid mapping between the grayscale CT volume and a new test volume provided the deformation information for mapping the probabilistic atlas to the test CT volume. The evaluation with the 20 VISCERAL non-enhanced CT dataset showed that the proposed method yielded an average Dice coefficient of over 95% for the lungs, over 90% for the liver, as well as around 80% and 70% for the spleen and the kidneys respectively.