Cell Simulation for Validation of Cell Micrograph Evaluation Algorithms

Wiesmann, Veit; Sauer, Tobias; Held, Christian; Palmisano, Ralf; Wittenberg, Thomas

doi:10.1515/bmt-2013-4272

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…To generate a simulation of semen sample video for assessment and validation, we need the following: (1) a model for the image of a sperm cell (to generate sperm cells in the simulated semen image) and (2) a model for the sperm cell movement that defines how the semen image changes over time. The development of sperm cell model in this paper mirrors similar work that was done for other cell types [16][17][18][19][20][21]. The development of sperm cell swim models is informed by several existing approaches.…”

Section: Researchmentioning

confidence: 90%

An Assessment Tool for Computer-Assisted Semen Analysis (CASA) Algorithms

Choi

Alkhoury

Urbano

et al. 2022

Preprint

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Background: Computer-Assisted Semen Analysis (CASA) enables reliable analysis of semen images, and is designed to process large number of images with high consistency, accuracy, and repeatability. Design and testing of CASA algorithms can be accelerated greatly if reliable simulations of semen images under a variety of conditions and sample quality modes are available. Using lifelike simulation of semen images can quantify the performance of existing and proposed CASA algorithms, since the parameters of the simulated image are known and controllable. Methods: We present simulation models for sperm cell image and swimming modes observed in real 2D (top-down) images of sperm cells in laboratory specimen. The models simulate human sperm using four (4) types of swimming, namely linear mean, circular, hyperactive, and immotile (or dead). Results: The simulation models are used in studying algorithms for segmentation, localization, and tracking of sperm cells. Several segmentation and localization algorithms were tested under varying levels of noise, and then compared using precision, recall, and the optimal subpattern assignment (OSPA) metric. Images of real human semen sample were used to validate the segmentation and localization observations obtained from simulations. An example is given of sperm cell tracking on simulated semen images of cells using the different tracking algorithms (nearest neighbor (NN), global nearest neighbor (GNN), probabilistic data association filter (PDAF), and joint probabilistic data association filter (JPDAF)). Tracking performance was evaluated through multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA). Conclusion: Simulation models enable objective assessments of semen image processing algorithms. We demonstrate the use of a new simulation tool to assess and compare segmentation, localization, and tracking methods. The simulation software allows testing along a large spectrum of parameter values that control the appearance and behavior of simulated semen images. Users can generate scenarios of different characteristics and assess the effectiveness of different CASA algorithms in these environments. The simulation was used to assess and compare algorithms for segmentation and tracking of sperm cells in semen images.

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Section: Researchmentioning

confidence: 90%

An Assessment Tool for Computer-Assisted Semen Analysis (CASA) Algorithms

Choi

Alkhoury

Urbano

et al. 2022

Preprint

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“…In order to have a test data set with a structure similar to that of the experimental data D1–D4, we are extending a previously proposed cell simulation software for the computation of simulated cell colony movies (DS1–DS5) (Wiesmann et al, 2013 ). Simulated movies can be downloaded from the study of Wiesmann et al ( 2017 ).…”

Section: Methodsmentioning

confidence: 99%

A Novel Methodology for Characterizing Cell Subpopulations in Automated Time-lapse Microscopy

Hattab

Wiesmann

Becker

et al. 2018

Front. Bioeng. Biotechnol.

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Time-lapse imaging of cell colonies in microfluidic chambers provides time series of bioimages, i.e., biomovies. They show the behavior of cells over time under controlled conditions. One of the main remaining bottlenecks in this area of research is the analysis of experimental data and the extraction of cell growth characteristics, such as lineage information. The extraction of the cell line by human observers is time-consuming and error-prone. Previously proposed methods often fail because of their reliance on the accurate detection of a single cell, which is not possible for high density, high diversity of cell shapes and numbers, and high-resolution images with high noise. Our task is to characterize subpopulations in biomovies. In order to shift the analysis of the data from individual cell level to cellular groups with similar fluorescence or even subpopulations, we propose to represent the cells by two new abstractions: the particle and the patch. We use a three-step framework: preprocessing, particle tracking, and construction of the patch lineage. First, preprocessing improves the signal-to-noise ratio and spatially aligns the biomovie frames. Second, cell sampling is performed by assuming particles, which represent a part of a cell, cell or group of contiguous cells in space. Particle analysis includes the following: particle tracking, trajectory linking, filtering, and color information, respectively. Particle tracking consists of following the spatiotemporal position of a particle and gives rise to coherent particle trajectories over time. Typical tracking problems may occur (e.g., appearance or disappearance of cells, spurious artifacts). They are effectively processed using trajectory linking and filtering. Third, the construction of the patch lineage consists in joining particle trajectories that share common attributes (i.e., proximity and fluorescence intensity) and feature common ancestry. This step is based on patch finding, patching trajectory propagation, patch splitting, and patch merging. The main idea is to group together the trajectories of particles in order to gain spatial coherence. The final result of CYCASP is the complete graph of the patch lineage. Finally, the graph encodes the temporal and spatial coherence of the development of cellular colonies. We present results showing a computation time of less than 5 min for biomovies and simulated films. The method, presented here, allowed for the separation of colonies into subpopulations and allowed us to interpret the growth of colonies in a timely manner.

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“…One requirement when comparing normalization methods and workflows is the need for a ground truth dataset. One approach is to generate and use simulated data images with a known ground truth to judge and compare methods and workflows ( Svoboda, 2009 ; Ulman, 2016 ; Watabe, 2015 ; Wiesmann, 2013 , 2017 ; Wiesner, 2019 ). A drawback of the simulated data approaches is the reliance on a theoretical error model.…”

Section: Introductionmentioning

confidence: 99%

FLINO: a new method for immunofluorescence bioimage normalization

et al. 2021

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Motivation Multiplexed immunofluorescence bioimaging of single-cells and their spatial organization in tissue holds great promise to the development of future precision diagnostics and therapeutics. Current multiplexing pipelines typically involve multiple rounds of immunofluorescence staining across multiple tissue slides. This introduces experimental batch effects that can hide underlying biological signal. It is important to have robust algorithms that can correct for the batch effects while not introducing biases into the data. Performance of data normalization methods can vary among different assay pipelines. To evaluate differences, it is critical to have a ground truth dataset that is representative of the assay. Results A new immunoFLuorescence Image NOrmalization (FLINO) method is presented and evaluated against alternative methods and workflows. Multi-round immunofluorescence staining of the same tissue with the nuclear dye DAPI was used to represent virtual slides and a ground truth. DAPI was re-stained on a given tissue slide producing multiple images of the same underlying structure but undergoing multiple representative tissue handling steps. This ground truth dataset was used to evaluate and compare multiple normalization methods including median, quantile, smooth quantile, median ratio normalization (MRN) and trimmed mean of the M-values (TMM). These methods were applied in both an unbiased grid object and segmented cell object workflow to 24 multiplexed biomarkers. An upper quartile normalization of grid objects in log space was found to obtain almost equivalent performance to directly normalizing segmented cell objects by the middle quantile. The developed grid-based technique was then applied with on-slide controls for evaluation. Using five or fewer controls per slide can introduce biases into the data. Ten or more on-slide controls were able to robustly correct for batch effects. Supplementary information Supplementary data are available at Bioinformatics online.

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Cell Simulation for Validation of Cell Micrograph Evaluation Algorithms

Cited by 5 publications

References 3 publications

An Assessment Tool for Computer-Assisted Semen Analysis (CASA) Algorithms

An Assessment Tool for Computer-Assisted Semen Analysis (CASA) Algorithms

A Novel Methodology for Characterizing Cell Subpopulations in Automated Time-lapse Microscopy

FLINO: a new method for immunofluorescence bioimage normalization

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