1. A framework for feedback-based segmentation of 3D image stacks
- Author
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Ralf Mikut, Ira V. Mang, Johannes Stegmaier, Heike Leitte, Markus Reischl, Julia Portl, Rasmus R. Schröder, and Nico Peter
- Subjects
0301 basic medicine ,Computer science ,Segmentation-based object categorization ,business.industry ,DATA processing & computer science ,Biomedical Engineering ,Automated segmentation ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Scale-space segmentation ,Image segmentation ,Accurate segmentation ,03 medical and health sciences ,030104 developmental biology ,3d image ,3D imaging ,accurate segmentation ,Medicine ,Computer vision ,Segmentation ,Artificial intelligence ,automated segmentation ,ddc:004 ,business - Abstract
3D segmentation has become a widely used technique. However, automatic segmentation does not deliver high accuracy in optically dense images and manual segmentation lowers the throughput drastically. Therefore, we present a workflow for 3D segmentation being able to forecast segments based on a user-given ground truth. We provide the possibility to correct wrong forecasts and to repeatedly insert ground truth in the process. Our aim is to combine automated and manual segmentation and therefore to improve accuracy by a tunable amount of manual input.
- Published
- 2016
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