Paper Info
Title
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.
Year
DOI
Venue
2018
10.1007/978-3-662-56537-7_60
Bildverarbeitung für die Medizin
Field
DocType
Citations 
Pattern recognition,Point location,GrowCut algorithm,Segmentation,Computer science,Sørensen–Dice coefficient,Image segmentation,Graphical user interface,Ground truth,Artificial intelligence,User interface
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Mario Amrehn100.34
Maddalena Strumia210.68
Stefan Steidl3114079.71
Tim Horz411.02
Markus Kowarschik522242.67
Andreas K. Maier6560178.76