Generating semi-synthetic validation benchmarks for embryomics

Stegmaier, Johannes; Arz, Julian; Schott, Benjamin; Otte, Jens C.; Kobitski, Andrei Yu; Nienhaus, G. Ulrich; Strähle, Uwe; Sanders, Peter; Mikut, Ralf

doi:10.1109/isbi.2016.7493359

Cited by 16 publications

(19 citation statements)

References 18 publications

(30 reference statements)

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“…Details on the benchmark generation can be found in App. A and [46]. Abbreviations for the different algorithms are given in round brackets and a quantitative comparison of the result quality is provided in Sec.…”

Section: Extending and Enhancing Algorithms With Uncertainty Treatmentmentioning

confidence: 99%

“…To provide a thorough validation of the entire pipeline comprised of seed point detection, segmentation, multiview fusion and tracking, we used our recently presented approach for generating comprehensive validation benchmarks [46] with an adapted object movement simulation. In brief, object locations, object movements and object interactions were simulated over multiple time points to obtain movement behaviors that resembled biological specimens.…”

Section: A Validation Benchmarkmentioning

confidence: 99%

“…11A). Analogous to the formulation used in [46], the total displacement vector of a single object at a given time point can be summarized to: Figure 11: Pipeline schematic of the benchmark simulation. (A) Illustration of the embryo simulation.…”

Section: A Validation Benchmarkmentioning

confidence: 99%

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Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Stegmaier

Mikut

2017

PLoS ONE

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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.

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Section: Extending and Enhancing Algorithms With Uncertainty Treatmentmentioning

confidence: 99%

Section: A Validation Benchmarkmentioning

confidence: 99%

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Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Stegmaier

Mikut

2017

PLoS ONE

Self Cite

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“…An initial version of the pipeline is implemented in the open-source software XPIWIT [1]. To improve the generalization capabilities of the CNN and to ideally make it work across different data sets and across specimens, we plan to train the network on both manually annotated and synthetic 3D microscopy data that represent and mimic various kinds of imaging circumstances [14,17]. In the current implementation, the preprocessing, the region merging and the over-segmentation correction modules are implemented in C++, MATLAB and Python.…”

Section: Resultsmentioning

confidence: 99%

Cell segmentation in 3D confocal images using supervoxel merge-forests with CNN-based hypothesis selection

Stegmaier

Spina

Falcão

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

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Automated segmentation approaches are crucial to quantitatively analyze large-scale 3D microscopy images. Particularly in deep tissue regions, automatic methods still fail to provide error-free segmentations. To improve the segmentation quality throughout imaged samples, we present a new supervoxel-based 3D segmentation approach that outperforms current methods and reduces the manual correction effort. The algorithm consists of gentle preprocessing and a conservative super-voxel generation method followed by supervoxel agglomeration based on local signal properties and a postprocessing step to fix under-segmentation errors using a Convolutional Neural Network. We validate the functionality of the algorithm on manually labeled 3D confocal images of the plant Arabidopis thaliana and compare the results to a state-of-the-art meristem segmentation algorithm.

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“…Although they attempt to provide 3D tracking of cells in early developing zebrafish images, the cells are displayed there as few‐pixel‐wide blobs. Recently, Stegmaier et al published an excellent 3D + t simulation of zebrafish embryo development for the evaluation of cell tracking. The proposed framework (written in Matlab®) was capable of mimicking spatio‐temporal cell dynamics, including cell division events.…”

Section: Simulation At Various Scalesmentioning

confidence: 99%

Virtual cell imaging: A review on simulation methods employed in image cytometry

et al. 2016

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The simulations of cells and microscope images thereof have been used to facilitate the development, selection, and validation of image analysis algorithms employed in cytometry as well as for modeling and understanding cell structure and dynamics beyond what is visible in the eyepiece. The simulation approaches vary from simple parametric models of specific cell components-especially shapes of cells and cell nuclei-to learning-based synthesis and multi-stage simulation models for complex scenes that simultaneously visualize multiple object types and incorporate various properties of the imaged objects and laws of image formation. This review covers advances in artificial digital cell generation at scales ranging from particles up to tissue synthesis and microscope image simulation methods, provides examples of the use of simulated images for various purposes ranging from subcellular object detection to cell tracking, and discusses how such simulators have been validated. Finally, the future possibilities and limitations of simulation-based validation are considered. © 2016 International Society for Advancement of Cytometry.

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Generating semi-synthetic validation benchmarks for embryomics

Cited by 16 publications

References 18 publications

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Cell segmentation in 3D confocal images using supervoxel merge-forests with CNN-based hypothesis selection

Virtual cell imaging: A review on simulation methods employed in image cytometry

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