Volumetric analysis of respiratory gated whole lung and liver CT data with motion-constrained graph cuts segmentation

The conventional graph cuts technique has been widely used for image segmentation due to its ability to find the global minimum and its ease of implementation. However, it is an intensity-based technique and as a result is limited to segmentation applications where there is significant contrast between the object and the background. We modified the conventional graph cuts method by adding shape prior and motion information. Active shape models (ASM) with signed distance functions were used to capture the shape prior information, preventing unwanted surrounding tissue from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend 3D segmentation to 4D by warping a prior shape model through time. The method has been applied to segmentation of whole lung boundary and whole liver boundary from respiratory gated CT data. 4D lung boundary segmentation in five patients, and 4D liver boundary segmentation in five patients were performed and in each case, results were compared with the results from expert-delineated ground truth. 4D segmentation for five phases of CT data took approximately ten minutes on a PC workstation with AMD Phenom II and 32GB of memory. An important by-product is quantitative whole organ volumes from respiratory gated CT from end-inspiration to end-expiration which can be determined with high accuracy.