Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
Segmentation by Grouping Junctions
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Isoperimetric Graph Partitioning for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Vessel Centerline Extraction Algorithm for Catheter Simulation
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Star Shape Prior for Graph-Cut Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
A graph-cut based algorithm for approximate MRF optimization
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
FIST: fast interactive segmentation of tumors
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
Manual Refinement System for Graph-Based Segmentation Results in the Medical Domain
Journal of Medical Systems
Segmentation of pituitary adenoma: A graph-based method vs. a balloon inflation method
Computer Methods and Programs in Biomedicine
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In this paper, a segmentation method for spherically-and elliptically-shaped objects is presented. It utilizes a user-defined seed point to set up a directed 3D graph. The nodes of the 3D graph are obtained by sampling along rays that are sent through the surface points of a polyhedron. Additionally, several arcs and a parameter constrain the set of possible segmentations and enforce smoothness. After the graph has been constructed, the minimal cost closed set on the graph is computed via a polynomial time s-t cut, creating an optimal segmentation of the object. The presented method has been evaluated on 50 Magnetic Resonance Imaging (MRI) data sets with World Health Organization (WHO) grade IV gliomas (glioblastoma multiforme). The ground truth of the tumor boundaries were manually extracted by three clinical experts (neurological surgeons) with several years ( 6) of experience in resection of gliomas and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 80.37±8.93%. However, no segmentation method provides a perfect result, so additional editing on some slices was required, but these edits could be achieved quickly because the automatic segmentation provides a border that fits mostly to the desired contour. Furthermore, the manual segmentation by neurological surgeons took 2-32 minutes (mean: 8 minutes), in contrast to the automatic segmentation with our implementation that took less than 5 seconds.