Multi-organ segmentation using shape model guided local phase analysis

Abstract

To improve the accuracy of multi-organ segmentation, we propose a model-based segmentation framework that utilizes the local phase information from paired quadrature filters to delineate the organ boundaries. Conventional local phase analysis based on local orientation has the drawback of outputting the same phases for black-to-white and white-to-black edges. This ambiguity could mislead the segmentation when two organs’ borders are too close. Using the gradient of the signed distance map of a statistical shape model, we could distinguish between these two types of edges and avoid the segmentation region leaking into another organ. In addition, we propose a level-set solution that integrates both the edge-based (represented by local phase) and region-based speed functions. Compared with previously proposed methods, the current method uses local adaptive weighting factors based on the confidence of the phase map (energy of the quadrature filters) instead of a global weighting factor to combine these two forces. In our preliminary studies, the proposed method outperformed conventional methods in terms of accuracy in a number of organ segmentation tasks.