Application of fluorescence spectroscopy: excited-state dynamics, food-safety, and disease diagnosis

Fluorescence spectroscopy has been widely used in the study of the structure and dynamics of molecules in complex systems. Steady-state and time-resolved fluorescence methods are commonly used to gain insight into the chemical surroundings of the fluorophore. This thesis discusses a range of complex systems and phenomena that may fruitfully be examined by means of fluorescence spectroscopy, in particular: steady-state fluorescence, fluorescence quenching, fluorescence lifetime, time-resolved fluorescence anisotropy, fluorescence resonance energy transfer (FRET), and excited-state solvation dynamics. This thesis focuses on the interactions of fluorophores with biologically and environmentally important macromolecules, hydrogen atom transfer in the excited-state of medicinal pigment, and use of fluorescence from tissues for food-safety and disease diagnosis

Template-based 3D-2D rigid registration of vascular structures in frequency domain from a single view

Image guided interventions in angiography are performed with a real-time X-ray sequences acquired by a C-arm device which provides the surgeon two dimensional visualization needed to guide the surgical instruments. This visualization may be augmented by registering a three dimensional preoperative volume with the interventional images to provide additional information such as depth, removal of occlusions and alternative views of vessel paths. This thesis presents two novel methods for rigid registration of vascular structures in the preoperative volume to the interventional X-ray image for enhancing visualization in Image Guided Interventions. In the first part of this thesis, estimation of rotation and translation are decoupled. Rotation is estimated by comparing rotated projections of the segmented vessels of the volume with segmented X-ray vessels in frequency domain. Translation is then estimated by minimizing the distances and maximizing the overlap ratio between segmented vessels. The registration results are reported in mean Projection Distances. The second part of the thesis adds separation of out-of-plane translation estimation to the first part and replaces segmentation by gradients. Rotation and out-of-plane translation are estimated by comparing rotational projected templates of volume with depth templates formed by scaling the X-ray image in the Fourier Magnitude Domain. The in-plane translation is then estimated by a Fourier Phase correlation. The registration results are evaluated by a Gold Standard dataset on cerebral arteries. This method is robust against occlusions and noises due to its usage of gradients and frequency domain similarity, has high capture range and fast, fixed computation times for every step due to template based framework

Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Modelling Biological Shape Variability

International audienceComputational anatomy is an emerging discipline at the interface of geometry, statistics and image analysis which aims at modeling and analyzing the biological shape of tissues and organs. The goal is to estimate representative organ anatomies across diseases, populations, species or ages, to model the organ development across time (growth or aging), to establish their variability, and to correlate this variability information with other functional, genetic or structural information. The Mathematical Foundations of Computational Anatomy (MFCA) workshop aims at fostering the interactions between the mathematical community around shapes and the MICCAI community in view of computational anatomy applications. It targets more particularly researchers investigating the combination of statistical and geometrical aspects in the modeling of the variability of biological shapes. The workshop is a forum for the exchange of the theoretical ideas and aims at being a source of inspiration for new methodological developments in computational anatomy. A special emphasis is put on theoretical developments, applications and results being welcomed as illustrations. Following the successful rst edition of this workshop in 20061 and second edition in New-York in 20082, the third edition was held in Toronto on September 22 20113. Contributions were solicited in Riemannian and group theoretical methods, geometric measurements of the anatomy, advanced statistics on deformations and shapes, metrics for computational anatomy, statistics of surfaces, modeling of growth and longitudinal shape changes. 22 submissions were reviewed by three members of the program committee. To guaranty a high level program, 11 papers only were selected for oral presentation in 4 sessions. Two of these sessions regroups classical themes of the workshop: statistics on manifolds and diff eomorphisms for surface or longitudinal registration. One session gathers papers exploring new mathematical structures beyond Riemannian geometry while the last oral session deals with the emerging theme of statistics on graphs and trees. Finally, a poster session of 5 papers addresses more application oriented works on computational anatomy

Fully automatic image analysis framework for cervical vertebra in X-ray images

Despite the advancement in imaging technologies, a fifth of the injuries in the cervical spine remain unnoticed in the X-ray radiological exam. About a two-third of the subjects with unnoticed injuries suffer tragic consequences. Based on the success of computer-aided systems in several medical image modalities to enhance clinical interpretation, we have proposed a fully automatic image analysis framework for cervical vertebrae in X-ray images. The framework takes an X-ray image as input and highlights different vertebral features at the output. To the best of our knowledge, this is the first fully automatic system in the literature for the analysis of the cervical vertebrae. The complete framework has been built by cascading specialized modules, each of which addresses a specific computer vision problem. This dissertation explores data-driven supervised machine learning solutions to these problems. Given an input X-ray image, the first module localizes the spinal region. The second module predicts vertebral centers from the spinal region which are then used to generate vertebral image patches. These patches are then passed through machine learning modules that detect vertebral corners, highlight vertebral boundaries, segment vertebral body and predict vertebral shapes. In the process of building the complete framework, we have proposed and compared different solutions to the problems addressed by each of the modules. A novel region-aware dense classification deep neural network has been proposed for the first module to address the spine localization problem. The proposed network outperformed the standard dense classification network and random forestbased methods. Location of the vertebral centers and corners vary based on human interpretation and thus are better represented by probability maps than single points. To learn the mapping between the vertebral image patches and the probability maps, a novel neural network capable of predicting a spatially distributed probabilistic distribution has been proposed. The network achieved expert-level performance in localizing vertebral centers and outperform the Harris corner detector and Hough forest-based methods for corner localization. The proposed network has also shown its capability for detecting vertebral boundaries and produced visually better results than the dense classification network-based boundary detectors. Segmentation of the vertebral body is a crucial part of the proposed framework. A new shapeaware loss function has been proposed for training a segmentation network to encourage prediction of vertebra-like structures. The segmentation performance improved significantly, however, the pixel-wise nature of proposed loss function was not able to constrain the predictions adequately. To solve the problem a novel neural network was proposed which predicts vertebral shapes and trains on a loss function defined in the shape space. The proposed shape predictor network was capable of learning better topological information about the vertebra than the shape-aware segmentation network. The methods proposed in this dissertation have been trained and tested on a challenging dataset of X-ray images collected from medical emergency rooms. The proposed, first-of-its-kind, fully automatic framework produces state-of-the-art results both quantitatively and qualitatively

The ecological fate of microplastic in the nearshore environment of South Georgia, a sub-Antarctic island

Microplastic is a marine pollutant of global concern which has managed to penetrate remote regions. This thesis describes the first comprehensive assessment of microplastics in the nearshore environment of South Georgia, an island in the sub-Antarctic region of the South Atlantic and Southern Ocean. The following samples were collected and analysed for their microplastic contents: seawater sampled from the coast and offshore, wastewater from land-based human habitation, precipitation, zooplankton, fish (Lepidonotothen larseni, Gobionotothen gibberifrons, Patagonotothen guntheri, and Gymnoscopelus bolini), and scats from two breeding populations of higher predators (Arctocephalus gazella and Pygoscelis papua), which were also examined for their dietary composition. The concentration of microplastic in seawater was 0.58 ± 5.17 particles L-1 (mean ± standard deviation, median = 0, range = 0 – 4), higher than many other records of microplastics in surface seawater from the Southern Ocean. There was little similarity between the type of microplastics retrieved from seawater, wastewater (0.55 ± 3.00 L-1 mean ± s.d., median = 0.33, range = 0 – 2.33) and precipitation (1.55 ± 3.21 L-1 mean ± s.d., median = 1.16, range = 0 – 2.33). The microplastic concentration in zooplankton was 1.6 ± 1.6 particles per 15 g, and microplastic was found in every year examined with no significant change in concentration over time. Two microplastics were retrieved from fish, and the concentration in higher predators was 0.04 ± 0.05 particles g-1 (mean ± s.d., median = 0.025, range = 0 – 0.1) of scat in A. gazella and 0.08 ± 0.09 particles g-1 (mean ± s.d, median = 0.05, range = 0 – 0.25) of scat in P. papua, greater than abundances recorded from the Antarctic Peninsula, but lower than reports from lower latitudes. Morphometric analysis of hard parts suggested fish and crustacean diets but little evidence of the trophic transfer of microplastics into predators from their prey. South Georgia is a biodiversity hotspot, the site of one of the world’s largest marine protected areas and has commercial importance from fishing and tourism. This thesis aims to contribute knowledge of the scale of anthropogenic stress on the region and produce a baseline, in terms of findings and best methodological practices, for any future research or monitoring of this pollutant in this region. Although wider ecological questions remain, the extent of microplastic in South Georgia nearshore waters has been quantified for the first time

Shear-promoted drug encapsulation into red blood cells: a CFD model and μ-PIV analysis

The present work focuses on the main parameters that influence shear-promoted encapsulation of drugs into erythrocytes. A CFD model was built to investigate the fluid dynamics of a suspension of particles flowing in a commercial micro channel. Micro Particle Image Velocimetry (μ-PIV) allowed to take into account for the real properties of the red blood cell (RBC), thus having a deeper understanding of the process. Coupling these results with an analytical diffusion model, suitable working conditions were defined for different values of haematocrit

Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis

[no abstract

A Perfect Match Condition for Point-Set Matching Problems Using the Optimal Mass Transport Approach

We study the performance of optimal mass transport--based methods applied to point-set matching problems. The present study, which is based on the L2 mass transport cost, states that perfect matches always occur when the product of the point-set cardinality and the norm of the curl of the nonrigid deformation field does not exceed some constant. This analytic result is justified by a numerical study of matching two sets of pulmonary vascular tree branch points whose displacement is caused by the lung volume changes in the same human subject. The nearly perfect match performance verifies the effectiveness of this mass transport--based approach.Read More: http://epubs.siam.org/doi/abs/10.1137/12086443