Multi-organ segmentation in abdominal CT images.

Automated segmentation of multiple organs in CT data of the upper abdomen is addressed. In order to explicitly incorporate the spatial interrelations among organs, we propose a method for finding and representing the interrelations based on canonical correlation analysis. Furthermore, methods are developed for constructing and utilizing the statistical atlas in which inter-organ constraints are explicitly incorporated to improve accuracy of multi-organ segmentation. The proposed methods were tested to perform segmentation of eight abdominal organs (liver, spleen, kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from various imaging conditions of CT datasets. 87 datasets acquired at two institutions were used for the validation. Significant accuracy improvement was observed for several organs in comparison with the conventional method.