Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research

Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines

Many automatically analyzable scientific questions are well-posed and offer a variety of information about the expected outcome a priori. Although often being 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 the direct information about the ambiguity inherent in the extracted data. We present a new concept for the estimation and propagation of uncertainty involved in image analysis operators. This allows using simple processing operators that are suitable for analyzing large-scale 3D+t microscopy images without compromising the result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it enhance the result quality of various processing operators. All presented concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. Furthermore, the functionality of the proposed approach is 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. Especially, the automated analysis of terabyte-scale microscopy data will benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. The generality of the concept, however, makes it also applicable to practically any other field with processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure

Automatic Computer Vision Systems for Aquatic Research

Recently, there has been an increase in biological research interest in fish, and zebrafish, as an efficient model in the investigation of a broad range of human diseases and genetic studies. Economically, the enormous number, low price and limited maintenance requirements of this fish species encouraged the researchers to use it extensively. The larva of this animal is also considered to be promising subjects for research that is not subject to same strict legal requirements as the adult fish. The importance of this animal in research has increased the demand for developing new computer vision tools and methods that could help researchers to perform more related investigations as well as understand behaviour for different experimental tests. Computer vision is an efficient, economical and non-intrusive tool that can be applied to research in aquatic laboratories and aquaculture environments. However, in marine applications, this technology is still facing big challenges due to the free-swimming nature and unpredictable behaviour of the fish. This thesis presents a suite of novel and cost-effective tools for fish tracking and behavioural analysis, sizing, and identification of individual zebrafish. These main contributions this work is outlined briefly as follows. The first part of this work deals with stimulation and physical activity analysis for fish larvae, a novel robust and automated multiple fish larva tracking system is proposed. The system is capable of tracking twenty-five fish larvae simultaneously and extracting all physical activity parameters such as; speed, acceleration, path, moved distance and active time. The system is used for further studies throughout local occurrence behaviour recognition and studying the behavioural of the fish larvae following electrical, chemical and thermal stimulation. The proposed tracking system has been adopted in the biologists' aquatic laboratory to be used as a robust tool for fish behaviour analysis when fish are exposed to several types of stimulation. In the second part of the work, two novel practical and cost-effective models; orthogonal and stereo systems are designed and implemented to estimate the length of small free-swimming fish using off-the-shelf-components. The designed models are accurate and easy to adapt use for small experimental tanks in laboratory settings. The models have been thoroughly tested and validated experimentally. The third part of this thesis offers novel non-contact methods for recognition of individual free-swimming fish. Such systems can significantly reduce experts efforts and time required for fish tagging process and also offer a real-time recognition technique that can be alternative to the existing tagging methods used in this field. Through the purposes of this suite of novel computer vision tools and models, this thesis has provided successful solutions for behavioural analysis, fish sizing, individual fish recognition related research problems. The proposed solutions addressed major research problems and provided novel and cost-effective solutions for these problems

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work 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

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

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.Comment: 218 pages, 58 figures, PhD thesis, Department of Mechanical Engineering, Karlsruhe Institute of Technology, published online with KITopen (License: CC BY-SA 3.0, http://dx.doi.org/10.5445/IR/1000057821

Social Eavesdropping in Zebrafish

Group living animals may eavesdrop on signalling interactions between conspecifics. This enables them to collect adaptively relevant information about others, without incurring in the costs of first-hand information acquisition. Such ability, aka social eavesdropping, is expected to impact Darwinian fitness and hence predicts the evolution of cognitive processes that enable social animals to use social information available in the environment.(...

Genome engineering of isogenic human ES cells to model autism disorders.

Isogenic pluripotent stem cells are critical tools for studying human neurological diseases by allowing one to study the effects of a mutation in a fixed genetic background. Of particular interest are the spectrum of autism disorders, some of which are monogenic such as Timothy syndrome (TS); others are multigenic such as the microdeletion and microduplication syndromes of the 16p11.2 chromosomal locus. Here, we report engineered human embryonic stem cell (hESC) lines for modeling these two disorders using locus-specific endonucleases to increase the efficiency of homology-directed repair (HDR). We developed a system to: (1) computationally identify unique transcription activator-like effector nuclease (TALEN) binding sites in the genome using a new software program, TALENSeek, (2) assemble the TALEN genes by combining golden gate cloning with modified constructs from the FLASH protocol, and (3) test the TALEN pairs in an amplification-based HDR assay that is more sensitive than the typical non-homologous end joining assay. We applied these methods to identify, construct, and test TALENs that were used with HDR donors in hESCs to generate an isogenic TS cell line in a scarless manner and to model the 16p11.2 copy number disorder without modifying genomic loci with high sequence similarity