Interactive 3D editing tools for image segmentation.
Segmentation is an important part of image processing, which often has a large impact on quantitative image analysis results. Fully automated operator independent segmentation procedures that successfully work in a population with a larger biological variation are extremely difficult to design and usually some kind of operator intervention is required, at least in pathological cases. We developed a variety of 3D editing tools that can be used to correct or improve results of initial automatic segmentation procedures. Specifically we will discuss and show examples for three types of editing tools that we termed: hole-filling (tool 1), point-bridging (tool 2), and surface-dragging (tool 3). Each tool comes in a number of flavors, all of which are implemented in a truly 3D manner. We describe the principles, evaluate efficiency and flexibility, and discuss advantages and disadvantages of each tool. We further demonstrate the superiority of the 3D approach over the time-consuming slice-by-slice editing of 3D datasets, which is still widely used in medical image processing today. We conclude that performance criteria for automatic segmentation algorithms may be eased significantly by including 3D editing tools early in the design process.
Kang
Yan
Y
Institute of Medical Physics, University of Erlangen-Nürnberg, Kraukenhausstr. 12, Erlangen 91054, Germany.
Engelke
Klaus
K
Kalender
Willi A
WA
D000465:Algorithms; D005269:Femur / Q000033:anatomy & histology*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009862:Online Systems; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface