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| Stegmaier J. New Methods to Improve Large-Scale Microscopy Image Analysis...2016.pdf | 25.0 MB |
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SOURCE: Stegmaier J. New Methods to Improve Large-Scale Microscopy Image Analysis...2016
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MEDIAINFO
Textbook in PDF format Recent developments in the area of multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. For instance, in biological applications, time-resolved 3D light-sheet microscopy and serial section electron microscopy provide unprecedented possibilities ranging from in toto analyses of embryonic development down to investigations of subcellular processes or reconstructions of the nervous system. The routinely produced datasets in the terabyte-range, however, can hardly be analyzed manually. Thus, the extensive use of image analysis-based automation is an essential key to the success of the performed imaging experiments. Existing algorithms for such analysis tasks are mostly not directly applicable to these large-scale datasets and either have to be confined to small excerpts of the data or require an immense amount of computation capacities and execution time. Moreover, available prior knowledge that could be exploited for advanced analyses is often not sufficiently considered by automatic processing pipelines. The major contributions of the present thesis are a new concept for the estimation and propagation of uncertainty involved in image analysis operators and the development of new segmentation algorithms that are suitable for terabytescale analyses of 3D+t microscopy images. Based on fuzzy set theory, available a priori knowledge was transformed into a mathematical representation and extensively used to enhance the performance of processing operators by data filtering, uncertainty propagation and explicit exploitation of information uncertainty for result improvements. To target the need for efficient image analysis operators, three new segmentation algorithms were specifically developed to detect a generalized geometric class of objects, namely, spherical objects, line-like objects and locally plane-like objects. The developed pipelines were specifically tuned to be applicable to large-scale analyses, i.e., only fast and memory efficient processing operators were used in the implementation. Using an exemplary pipeline, it is demonstrated how a combination of both the fast algorithms and the proposed uncertainty framework could be used to further enhance the overall quality of the considered processing operators. All developed methods were thoroughly validated on existing and newly developed simulated benchmarks, to be able to quantitatively assess their applicability to different imaging conditions. In addition, the efficient implementations of all developed algorithms are presented and were made accessible to the community as platform independent open-source software tools. The new methods were successfully applied to multiple large-scale analyses of fluorescence microscopy images in the field of developmental biology. In particular, the proposed pipelines were used to quantify the impact of both known and unknown chemical substances on the neuronal development in the spinal cord of zebrafish in 2D images. Furthermore, the developed methods were applied to time-resolved 3D images to detect, segment and track fluorescently labeled cellular nuclei of entire zebrafish embryos and to quantitatively characterize cell morphology dynamics using fluorescently labeled cellular membranes in 3D+t microscopy images of fruit fly, zebrafish and mouse embryos
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