Morphology-based Features for Adaptive Mitosis Detection of In Vitro Stem Cell Tracking Data

Becker, Tim; Madany, A.

doi:10.3414/me11-02-0038

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…Active contour methods have been successfully used to trace cells [36,37], cellular components [38] and animals [37], in high-contrast images, but because these methods search for compartment boundaries, they are less useful in noisy, low-contrast images and situations where cells often change direction [37]. Another commonly used segmentation method is a noise threshold, in which a specific intensity value is chosen and all intensities falling below this threshold value are deemed background noise [39,40]. There is often high variability in cell fluorescence, however, which can render thresholding methods ineffective.…”

Section: Introductionmentioning

confidence: 99%

Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density

Baker

Brasch

Manning

et al. 2014

J. R. Soc. Interface.

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ResearchCite this article: Baker RM, Brasch ME, Manning ML, Henderson JH. 2014 Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density. J. R. Soc. Understanding single and collective cell motility in model environments is foundational to many current research efforts in biology and bioengineering. To elucidate subtle differences in cell behaviour despite cell-to-cell variability, we introduce an algorithm for tracking large numbers of cells for long time periods and present a set of physics-based metrics that quantify differences in cell trajectories. Our algorithm, termed automated contour-based tracking for in vitro environments (ACTIVE), was designed for adherent cell populations subject to nuclear staining or transfection. ACTIVE is distinct from existing tracking software because it accommodates both variability in image intensity and multi-cell interactions, such as divisions and occlusions. When applied to low-contrast images from live-cell experiments, ACTIVE reduced error in analysing cell occlusion events by as much as 43% compared with a benchmark-tracking program while simultaneously tracking cell divisions and resulting daughter-daughter cell relationships. The large dataset generated by ACTIVE allowed us to develop metrics that capture subtle differences between cell trajectories on different substrates. We present cell motility data for thousands of cells studied at varying densities on shape-memory-polymer--based nanotopographies and identify several quantitative differences, including an unanticipated difference between two 'control' substrates. We expect that ACTIVE will be immediately useful to researchers who require accurate, long-time-scale motility data for many cells.

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Section: Introductionmentioning

confidence: 99%

Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density

Baker

Brasch

Manning

et al. 2014

J. R. Soc. Interface.

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“…The open source toolkit MITK-DI provides a comprehensive software framework for model-based analysis and interactive data exploration of diffusion MR images. In the last contribution of this focus theme, the use of models in the field of microscopic cell image analysis is illustrated [16]. Morphological features are extracted and analyzed automatically with the goal to detect adaptive mitosis of in vitro stem cell tracking data.…”

Section: Resultsmentioning

confidence: 99%

“…In the focus theme new approaches for the model-based segmentation and analysis of dual-energy CT images [9], radiographic images [10], time-of-flight, phasecontrast and diffusion MR images [11][12][13][14][15] as well as microscopic cell images [16] are presented as examples of the development in this challenging field.…”

Section: Discussionmentioning

confidence: 99%

Image Analysis and Modeling in Medical Image Computing

Handels

Deserno

Meinzer³

et al. 2012

Methods Inf Med

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Summary Background: Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. Objectives: In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Methods: Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Results: Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. Conclusions: The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body. Hence, model-based image computing methods are important tools to improve medical diagnostics and patient treatment in future.

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“…However, available software fails to automatically detect and quantify high axon numbers and the morphological features of AxD (swellings and fragments). The reason may be two-fold: 1) Available software relies on image binarization 14,15 , which can lead to information loss and low sensitivity as thin axons may not be recognized. 2) The analysis requires subjective and time-consuming manual annotations, e.g., thresholding and defining the region of interest [16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

Deep learning to decipher the progression and morphology of axonal degeneration

Palumbo

Gruening

Landt

et al. 2020

Preprint

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Different axonal degeneration (AxD) patterns have been described depending on the biological condition. Until now, it remains unclear whether they are restricted to one specific condition or can occur concomitantly. Here, we present a novel microfluidic device in combination with a deep learning tool, the EntireAxon, for the high-throughput analysis of AxD. We evaluated the progression of AxD in an in vitro model of hemorrhagic stroke and observed that axonal swellings preceded axon fragmentation. We further identified four distinct morphological patterns of AxD (granular, retraction, swelling, and transport degeneration) that occur concomitantly. These findings indicate a morphological heterogeneity of AxD under pathophysiologic conditions. The newly developed microfluidic device along with the EntireAxon deep learning tool enable the systematic analysis of AxD but also unravel a so far unknown intricacy in which AxD can occur in a disease context.

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Morphology-based Features for Adaptive Mitosis Detection of In Vitro Stem Cell Tracking Data

Abstract: The proposed simple and label free adaptive variant might be the method of choice when it comes to autonomous cell farming. Hereby, it is essential to have reliable and unsupervised mitosis detection that covers all phases of cell growth.

Cited by 7 publications

References 13 publications

Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density

Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density

Image Analysis and Modeling in Medical Image Computing

Deep learning to decipher the progression and morphology of axonal degeneration

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