85 research outputs found
An optimal control approach to cell tracking
Cell tracking is of vital importance in many biological studies, hence robust cell tracking algorithms are needed for inference of dynamic features from (static) in vivo and in vitro experimental imaging data of cells migrating.
In recent years much attention has been focused on the modelling of cell motility from physical principles and the development of state-of-the art numerical methods for the simulation of the model equations. Despite this, the vast majority of cell tracking algorithms proposed to date focus solely on the imaging data itself and do not attempt to incorporate any physical knowledge on cell migration into the tracking procedure.
In this study, we present a mathematical approach for cell tracking, in which we formulate the cell tracking problem as an inverse problem for fitting a mathematical model for cell motility to experimental imaging data. The novelty of this approach is that the physics underlying the model for cell migration is encoded in the tracking algorithm. To illustrate this we focus on an example of Zebrafish (Danio rerio's larvae) Neutrophil migration and contrast an ad-hoc approach to cell tracking based on interpolation with the model fitting approach we propose in this study
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Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images
This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches
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Estimation of cellularity in tumours treated with Neoadjuvant therapy: A comparison of Machine Learning algorithms
This paper describes a method for residual tumour cellularity (TC) estimation in Neoadjuvant treatment (NAT) of advanced breast cancer. This is determined manually by visual inspection by a radiologist, then an automated computation will contribute to reduce time workload and increase precision and accuracy. TC is estimated as the ratio of tumour area by total image area estimated after the NAT. The method proposed computes TC by using machine learning techniques trained with information on morphological parameters of segmented nuclei in order to classify regions of the image as tumour or normal. The data is provided by the 2019 SPIE Breast challenge, which was proposed to develop automated TC computation algorithms. Three algorithms were implemented: Support Vector Machines, Nearest K-means and Adaptive Boosting (AdaBoost) decision trees. Performance based on accuracy is compared and evaluated and the best result was obtained with Support Vector Machines. Results obtained by the methods implemented were submitted during ongoing challenge with a maximum of 0.76 of prediction probability of success
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Morphological estimation of Cellularity on Neo-adjuvant treated breast cancer histological images
This paper describes a methodology that extracts morphological features from histological breast cancer images stained for Hematoxilyn and Eosin (H&E). Cellularity was estimated and the correlation between features and the residual tumour size cellularity after a Neo-Adjuvant treatment (NAT) was examined. Images from whole slide imaging (WSI) were processed automatically with traditional computer vision methods to extract twenty two morphological parameters from the nuclei, epithelial region and the global image. The methodology was applied to a set of images from breast cancer under NAT. The data came from the BreastPathQ Cancer Cellularity Challenge 2019, and consisted of 2579 patches of 255×255 pixels of H&E histopatological samples from NAT treatment patients. The methodology automatically implements colour separation, segmentation and morphological analysis using traditional algorithms (K-means grouping, watershed segmentation, Otsu’s binarisation). Linear regression methods were applied to determine strongest correlation between the parameters and the cancer cellularity. The morphological parameters showed correlation with the residual tumour cancer cellularity. The strongest correlations corresponded to the stroma concentration value (r = −0.9786) and value from HSV image colour space (r = −0.9728), both from a global image parameters
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Endogenous VEGF isoform expression regulates tumour cell motility
Background: Vascular endothelial growth factor-A (VEGF) is produced by most cancer cells as multiple alternatively spliced isoforms, which display distinct receptor and matrix binding characteristics. In addition to being a major inducer of tumour angiogenesis, VEGF also has complex functions in angiogenesis-independent aspects of tumour growth,but the role of individual VEGF isoforms remains poorly understood. Here we investigated the effects of endogenous VEGF isoform expression on tumour cell migration and invasion.
Method: We used a panel of mouse fibrosarcoma cells we developed (fs188, fs164 and fs120) that express single VEGF isoforms (188, 164 or 120 respectively), under endogenous promoter control. We investigated adhesion to different matrices, 2D migration and invasion through 3D collagen.
Results: Fs188 cells, are typically mesenchymal, form ruffles, display strong integrin-dependent adhesion and express high levels of pERK1/2 and pAKT. In contrast, fs164 and fs120 cells are not typically mesenchymal in morphology; they display weak binding to collagen, lack ruffles and align longitudinally forming long multicellular chains and abundant cell-cell contacts. On 3D collagen, fs188 cells remain mesenchymal while fs164 and fs120 cells adopt a rounded/amoeboid and a mix of rounded/mesenchymal morphologies respectively. Cell morphology and migration are dependent on the cytoskeleton and actinomyosin contractility, to provide traction force in mesenchymal movement, and cortical contraction for rounded amoeboid motility. Consistent with their mesenchymal characteristics, fs188 cells migrated faster in 2D and invaded 3D collagen more efficiently than fs164 or fs120 cells. Contractility inhibitors caused fs164/fs120 cells to switch to a mesenchymal morphology and accelerated their migration but not that of fs188 cells.
Conclusion: VEGF isoforms are emerging as potential biomarkers for anti-VEGF therapies. Our results suggest that individual VEGF isoforms influence the migration and invasion strategies of tumour cells thus adding to the complexity of VEGF signaling within the tumor microenvironment. Acknowledgements This work was funded by Cancer Research UK
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Corrigendum to 'Homage to Professor Maria Petrou' [ Pattern Recognition Letters 48 (2014) 2-7].
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Analysis of immune cell function using in vivo cell shape analysis and tracking
In this paper, a tracking and shape analysis algorithm for neutrophils of zebrafish is proposed. The neutrophils were fluorescently labelled with Green Fluorescent Protein and observed in a time-lapse three dimensional video through a confocal microscope. The neutrophils were segmented from the background and tracked with a keyhole model of movement. Morphological analysis was performed by calculating the volume of the segmented objects together with the measurements of sphericity, tortuosity and average number of end points of the centre lines. We speculate that these measurements are related to the activation of the neutrophils as part of the process of killing and digesting bacteria. The algorithm is fully automatic and should provide a robust frame- work of analysis for posterior analysis of the neutrophils in zebrafish
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β-glucan-dependent shuttling of conidia from neutrophils to macrophages occurs during fungal infection establishment
The initial host response to fungal pathogen invasion is critical to infection establishment and outcome. However, the diversity of leukocyte-pathogen interactions is only recently being appreciated. We describe a new form of interleukocyte conidial exchange called "shuttling." In Talaromyces marneffei and Aspergillus fumigatus zebrafish in vivo infections, live imaging demonstrated conidia initially phagocytosed by neutrophils were transferred to macrophages. Shuttling is unidirectional, not a chance event, and involves alterations of phagocyte mobility, intercellular tethering, and phagosome transfer. Shuttling kinetics were fungal-species-specific, implicating a fungal determinant. β-glucan serves as a fungal-derived signal sufficient for shuttling. Murine phagocytes also shuttled in vitro. The impact of shuttling for microbiological outcomes of in vivo infections is difficult to specifically assess experimentally, but for these two pathogens, shuttling augments initial conidial redistribution away from fungicidal neutrophils into the favorable macrophage intracellular niche. Shuttling is a frequent host-pathogen interaction contributing to fungal infection establishment patterns
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Measurement of Vascular Permeability from Multiphoton Microscopy of Experimental Tumours
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