New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
This addresses the problem of automated analysis for large-scale microscopy data in scientific research, though it appears incremental as it builds on existing segmentation and uncertainty methods.
The thesis tackles the challenge of analyzing terabyte-scale 3D+t microscopy images by introducing a new concept for uncertainty estimation and propagation in image analysis operators, along with new segmentation algorithms suitable for large-scale analyses.
Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced datasets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present thesis introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.