Abstract
Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout.
Highlights
Available prior knowledge is often not sufficiently considered by automatic processing pipelines
The adaptations required by a particular algorithm cannot be generalized, we present two potential applications: the fusion of redundant seed points and the correction of undersegmentation errors
To validate the proposed improvements of the LoG-based seed detection algorithm, the SBDE1, SBDE2 and SBDE3 benchmark data sets were used (S2 Table) with the parameters listed in S3 Table and the performance measures described in S3 Note
Summary
Available prior knowledge is often not sufficiently considered by automatic processing pipelines. Examples of the successful incorporation of prior knowledge are, e.g., the approaches described in [1, 2]; these make use of information about the expected object number as well as their associated physical size in order to adjust and improve seed point detection algorithms. Properties such as size, shape, geometry, intensity distributions and the like can be used to improve the performance of image segmentation algorithms [3,4,5]. We show qualitative results obtained with the presented framework on large-scale light-sheet microscopy data of developing zebrafish embryos
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