Nondyadic and nonlinear multiresolution image approximations

This thesis focuses on the development of novel multiresolution image approximations. Specifically, we present two kinds of generalization of multiresolution techniques: image reduction for arbitrary scales, and nonlinear approximations using other metrics than the standard Euclidean one. Traditional multiresolution decompositions are restricted to dyadic scales. As first contribution of this thesis, we develop a method that goes beyond this restriction and that is well suited to arbitrary scale-change computations. The key component is a new and numerically exact algorithm for computing inner products between a continuously defined signal and B-splines of any order and of arbitrary sizes. The technique can also be applied for non-uniform to uniform grid conversion, which is another approximation problem where our method excels. Main applications are resampling and signal reconstruction. Although simple to implement, least-squares approximations lead to artifacts that could be reduced if nonlinear methods would be used instead. The second contribution of the thesis is the development of nonlinear spline pyramids that are optimal for lp-norms. First, we introduce a Banach-space formulation of the problem and show that the solution is well defined. Second, we compute the lp-approximation thanks to an iterative optimization algorithm based on digital filtering. We conclude that l1-approximations reduce the artifacts that are inherent to least-squares methods; in particular, edge blurring and ringing. In addition, we observe that the error of l1-approximations is sparser. Finally, we derive an exact formula for the asymptotic Lp-error; this result justifies using the least-squares approximation as initial solution for the iterative optimization algorithm when the degree of the spline is even; otherwise, one has to include an appropriate correction term. The theoretical background of the thesis includes the modelisation of images in a continuous/discrete formalism and takes advantage of the approximation theory of linear shift-invariant operators. We have chosen B-splines as basis functions because of their nice properties. We also propose a new graphical formalism that links B-splines, finite differences, differential operators, and arbitrary scale changes

Computational insights into the influence of substrate stiffness on collective cell migration

Critically important biological phenomena in health and disease, such as wound healing, cancer metastasis, and embryonic development, are governed by collective cell migration. This highly complex process depends not only on cellular features, but also on different stimuli from the local cell environment. Cell migration is promoted by the combination of physico-chemical cues, including the mechanical properties of the extracellular matrix (ECM). Stiffness gradients within ECM have recently been demonstrated to result into preferred directions of cell migration. However, the specific mechanisms driving this directed collective cell migration and their relative roles remain unclear. Here, we develop a continuum formulation and its finite element (FE) implementation to test different hypotheses on the cause of spatial heterogeneities during cell migration on heterogeneous-stiffness substrates. We evaluate two key hypotheses: (i) cell polarisation is promoted by stiffness gradients within the ECM and; (ii) propulsion forces are weighted by ECM stiffness. Ultimately, we provide a robust in silico framework to explain experimental observations and guide future research.The authors thank Denis Wirtz (Johns Hopkins University) for relevant discussion. The authors acknowledge support from Programa de Apoyo a la Realizacion de Proyectos Interdiscisplinares de I+D para Jovenes Investigadores de la Universidad Carlos III de Madrid and Comunidad de Madrid, Spain (project: BIOMASKIN). DGG acknowledges support from the Talent Attraction grant, Spain (CM 2018 - 2018-T2/IND-9992) from the Comunidad de Madrid. This work was partially funded by projects TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministry of Economy and a 2017 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation, Spain

Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumes

Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting superior performance, or even human-level accuracy, compared to previous approaches on public mitochondria segmentation datasets. Unfortunately, many of these publications do not make neither the code nor the full training details public to support the results obtained, leading to reproducibility issues and dubious model comparisons. For that reason, and following a recent code of best practices for reporting experimental results, we present an extensive study of the state-of-the-art deep learning architectures for the segmentation of mitochondria on EM volumes, and evaluate the impact in performance of different variations of 2D and 3D U-Net-like models for this task. To better understand the contribution of each component, a common set of pre- and post-processing operations has been implemented and tested with each approach. Moreover, an exhaustive sweep of hyperparameters values for all architectures have been performed and each configuration has been run multiple times to report the mean and standard deviation values of the evaluation metrics. Using this methodology, we found very stable architectures and hyperparameter configurations that consistently obtain state-of-the-art results in the well-known EPFL Hippocampus mitochondria segmentation dataset. Furthermore, we have benchmarked our proposed models on two other available datasets, Lucchi++ and Kasthuri++, where they outperform all previous works. The code derived from this research and its documentation are publicly available

Quantitative Assessment of Emphysema Severity in Histological Lung Analysis

Published onlineEmphysema is a characteristic component of chronic obstructive pulmonary disease (COPD), which has been pointed out as one of the main causes of mortality for the next years. Animal models of emphysema are employed to study the evolution of this disease as well as the effect of treatments. In this context, measures such as the mean linear intercept (Lm) and the equivalent diameter (d) have been proposed to quantify the airspace enlargement associated with emphysematous lesions in histological sections. The parameter D2 , which relates the second and the third moments of the variable d , has recently shown to be a robust descriptor of airspace enlargement. However, the value of D2 does not provide a direct evaluation of emphysema severity. In our research, we suggest a Bayesian approach to map D2 onto a novel emphysema severity index (SI) reflecting the probability for a lung area to be emphysematous. Additionally, an image segmentation procedure was developed to compute the severity map of a lung section using the SI function. Severity maps corresponding to 54 lung sections from control mice, mice induced with mild emphysema and mice induced with severe emphysema were computed, revealing differences between the distribution of SI in the three groups. The proposed methodology could then assist in the quantification of emphysema severity in animal models of pulmonary disease.This work has been partly funded by the grants ‘‘MINECO DPI2012-38090-C03-02’’ and ‘‘TEC2013-48552-C2-1-R’’ from the Spanish Ministry of Economy and CompetitivenessPublicad

Spectral unmixing of multiply stained fluorescence samples T

The widespread use of fluorescence microscopy along with the vast library of available fluorescent stains and staining methods has been extremely beneficial to researchers in many fields, ranging from material sciences to plant biology. In clinical diagnostics, the ability to combine different markers in a given sample allows the simultaneous detection of the expression of several different molecules, which in turn provides a powerful diagnostic tool for pathologists, allowing a better classification of the sample at hand. The correct detection and separation of multiple stains in a sample is achieved not only by the biochemical and optical properties of the markers, but also by the use of appropriate hardware and software tools. In this chapter, we will review and compare these tools along with their advantages and limitations

Toward a morphodynamic model of the cell: Signal processing for cell modeling

From a systems biology perspective, the cell is the principal element of information integration. Therefore, understanding the cell in its spatiotemporal context is the key to unraveling many of the still unknown mechanisms of life and disease. This article reviews image processing aspects relevant to the quantification of cell morphology and dynamics. We cover both acquisition (hardware) and analysis (software) related issues, in a multiscale fashion, from the detection of cellular components to the description of the entire cell in relation to its extracellular environment. We then describe ongoing efforts to integrate all this vast and diverse information along with data about the biomechanics of the cell to create a credible model of cell morphology and behavior.Carlos Ortiz-de-Solorzano and Arrate Muñoz-Barrutia were supported by the Spanish Ministry of Economy and Competitiveness grants with reference DPI2012-38090-C03-02 and TEC2013-48552-C02, respectively. Michal Kozubek was supported by the Czech Science Foundation (302/12/G157)

Molecular imaging of pulmonary tuberculosis in an ex-vivo mouse model using spectral photon-counting computed tomography and micro-CT

Artículo escrito por un elevado número de autores. Sólo se referencian el que aparece en primer lugar y los autores pertenecientes a la UC3MThe results of this study support the idea that photon-counting CT imaging is capable of molecular imaging when enhanced by high-Z pharmaceuticals. The Medipix3RX detector operating four CSM energy bins provided sufficient spectral information for the simultaneous differentiation of iodine, water, and lipid (and a second high-Z contrast). In an ex-vivo mouse model of chronic TB, detection of iodine contrast enabled segmentation and volume quantification of healthy and disease-related lung tissue. The results demonstrated the potential clinical utility of photon-counting CT imaging for molecular imaging in infectious lung diseases. In the future, if a TB-specific drug were to be incorporated with a high-Z nanoparticle, spectral CT could provide non-invasive evaluation of drug delivery and response to treatment. Such an imaging platform would have the potential to assist diagnosis and accelerate the development of novel therapies, which is essential for the eradication of TB. Photon-counting CT technology offers improved spatial and energy resolution. Thus, it is a promising next step in the evolution of CT.The authors would like to acknowledge the Medipix2, Medipix3, and Medipix4 collaborations. They would also like to take this opportunity to acknowledge the generous support of the MARS Collaboration. They would also like to acknowledge Dr. Guembe from CIMA-Universidad de Navarra for preparing and staining the tissue sections

Innovations in ex vivo light sheet fluorescence microscopy

Light Sheet Fluorescence Microscopy (LSFM) has revolutionized how optical imaging of biological specimens can be performed as this technique allows to produce 3D fluorescence images of entire samples with a high spatiotemporal resolution. In this manuscript, we aim to provide readers with an overview of the field of LSFM on ex vivo samples. Recent advances in LSFM architectures have made the technique widely accessible and have improved its acquisition speed and resolution, among other features. These developments are strongly supported by quantitative analysis of the huge image volumes produced thanks to the boost in computational capacities, the advent of Deep Learning techniques, and by the combination of LSFM with other imaging modalities. Namely, LSFM allows for the characterization of biological structures, disease manifestations and drug effectivity studies. This information can ultimately serve to develop novel diagnostic procedures, treatments and even to model the organs physiology in healthy and pathological conditions.This work was produced with the support of the Spanish Ministry of Science, Innovation and Universities (TEC2016-78052-R, RTC-2017-6600-1, PID2019-109820RB-100, FPU19/02854)

The contribution of microfluidics to the fight against tuberculosis

The high mortality associated with tuberculosis brings forward the urgency of developing new therapies and strategies against the disease. With the advance of drug-resistant strains, traditional techniques have proven insufficient to manage the disease appropriately. Microfluidic devices have characteristics that can enhance treatment prescription and significantly advance our knowledge about the disease and its interaction within the human body. In addition, microfluidic systems provide advantages in terms of time and costs, which are particularly important in countries with low income and resources. This review will highlight how microdevices can help bridge the gaps in disease management, including their use for drug testing and development, drug susceptibility, basic research, and novel approaches to anti-TB vaccines and organ-on-chip studies.This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 853989. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and Global Alliance for TB Drug Development non-profit organisation, Bill & Melinda Gates Foundation and University of Dundee. This work was partially funded by Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación, under grant PID2019-109820RB-I00, MCIN/AEI/10.13039/501100011033/, co-finance by European Regional Development Fund (ERDF), "A way of making Europe