Ideal seed point location approximation for growcut interactive image segmentation

Abstract

The C-arm CT X-ray acquisition process is a common modality in medical imaging. After image formation, anatomical structures can be extracted via segmentation. Interactive segmentation methods bear the advantage of a dynamically adjustable trade-off between time and achieved segmentation quality for the object of interest w.r.t. fully automated approaches. The segmentation’s quality can be measured in terms of the Dice coefficient with the ground truth segmentation image. A user’s interaction traditionally consist of drawing pictorial hints on an overlay image to the acquired image data via a graphical user interface (UI). The quality of a segmentation utilizing a set of drawn seeds varies depending on the location of the seed points in the image. In this paper, we (1) investigate the influence of seed point location on segmentation quality and (2) propose an approximation framework for ideal seed placements utilizing an extension of the well established GrowCut segmentation algorithm and (3) introduce a user interface for the utilization of the suggested seed point locations. An extensive evaluation of the predictive power of seed importance is conducted from hepatic lesion input images. As a result, our approach suggests seed points with a median of 72.5% of the ideal seed points’ associated Dice scores, which is an increase of 8.4% points to sampling the seed location at random.