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A framework for feedback-based segmentation of 3D image stacks

Stegmaier, Johannes; Peter, Nico; Portl, Julia; Mang, Ira V.; Schröder, Rasmus; Leitte, Heike; Mikut, Ralf; Reischl, Markus



Abstract (englisch): 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.


Zugehörige Institution(en) am KIT Institut für Angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Jahr 2016
Sprache Englisch
Identifikator ISSN: 2364-5504
URN: urn:nbn:de:swb:90-602236
KITopen ID: 1000060223
HGF-Programm 47.01.02; LK 01
Erschienen in Current directions in biomedical engineering
Band 2
Heft 1
Seiten 437-441
Schlagworte 3D imaging, accurate segmentation, automated segmentation
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