Rule-based ventral cavity multi-organ automatic segmentation in CT scans

We describe a new method for the automatic segmentation of multiple organs of the ventral cavity in CT scans. The method is based on a set of rules that determine the order in which the organs are isolated and segmented, from the simplest one to the most difficult one. First, the body is isolated from the background. Second, the trachea and the left and right lungs are segmented based on their air content. Third, the spleen and the kidneys - the organs with high blood content - are segmented. Finally, the kidney is segmented based on the surrounding organs segmentation. Each organ is individually segmented with a four-step procedure that consists of: 1) definition of an inclusive region of interest; 2) identification of the largest axial cross-section slice; 3) removal of background structures by morphological operations, and; 4) 3D region growing segmentation. Our method is unique in that it uses the same generic segmentation approach for all organs and in that it relies on the segmentation difficulty of organs to guide the segmentation process. Experimental results on 15 CT scans of the VISCERAL Anatomy2 Challenge training datasets yield a Dice volume overlap similarity score of 79.1 for the trachea, 97.4 and 97.6 for the left and right lungs, 89.2 for the spleen, and 92.8 for the left kidney. For the 5 CT scans test datasets, the Dice scores are 97.9, 97.0, 85.6, 93.4 and 90.2, respectively. Our method achieved an overall DICE score of 92.8 and was ranked first among the five methods that participated in the challenge.