In the present contribution, we introduced a new approach for improved semi-automatic segmentation of large-scale 3D image stacks. The method does not require tedious parameter tuning and can help to significantly speed up segmentation tasks in scenarios where entirely automated processing is impossible. By propagating manual annotations to adjacent slices and by using sophisticated correction, a close to error-free segmentation can be achieved.
Further work will be put on the development of a graphical user interface that condenses all involved workflow steps and to extend the quantification of speed and quality improvements of the proposed segmentation approach. To improve the interactivity and usability of the framework we plan to develop an efficient C++-based application with highly interactive visualisation, editing and prediction capabilities as well as parallel execution of segment propagations in the background. The envisioned application will provide a powerful tool to enable detailed analyses of large-scale 3D microscopic image data sets.