Evolutionary history of ferns and the use of ferns and lycophytes in ecological studies

Tropical areas represent the region with the highest diversity in the world. In plants, one major radiation having this high diversity is ferns, the sister lineage of seed plants, which, together with lycophytes, another group of free-sporing vascular plants, comprise around 10,500 species in the world. Previous studies in the Neotropics have shown that edaphic conditions, such as nutrient concentration and soil texture, influence fern and lycophyte species diversity, local species composition and geographical distribution patterns. As a consequence, fern and lycophyte species are effective indicators of environmental characteristics of tropical forests. In this thesis, I studied three different aspects of ferns and lycophytes. First, I used ferns and lycophytes as an indicator group for animal behaviour. I explored the relationship between the home range size of three groups of tamarins (Leontocebus nigrifrons I. Geoffroy Saint-Hilaire) and forest productivity at Estación Biológica Quebrada Blanco. For this purpose, I identified the fern and lycophyte species present in the area, and using previous knowledge of their soil fertility preferences, I estimated the soil’s fertility within the tamarin home ranges. Contrary to my expectations, the home range size was not always negatively related to soil fertility. The group of tamarins inhabiting the poorest soils always had the biggest home range, but the group inhabiting the richest soils did not consistetly have the smallest home range, as it would be expected. Second, in order to improve the efficiency of ferns as indicator group, I revised the number of taxa present in the Neotropical fern genera Metaxya and Salpichlaena. I did this by combining morphological, molecular, phylogenetic and biogeographical studies. As a result, I delineated the boundaries of the taxa in these genera and described five new species and two subspecies, adding new records to Neotropical biodiversity. Third, I inferred fern phylogenetic relationships based on plastid genomes. Compared to more than 6,000 published plastomes of flowering plants, little is known about fern plastomes. Only around 130 fern plastomes have been published to date, with many important fern lineages completely unsampled. By applying Next-Generation Sequencing techniques, I generated eight new complete fern plastomes and built a phylogenetic hypothesis based on the protein coding regions of the newly produced and previously published plastomes. I was able to identify novel rearreagements in the genome structure revealing a contrasting evolutionary pattern between Polypodiineae and the other fern clades

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: (1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and (2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a second opinion for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by 77% w.r.t. learning-based segmentation methods using pixel-wise labels for training

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: (1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and (2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a second opinion for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ∼77% w.r.t. learning-based segmentation methods using pixel-wise labels for training

Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization with Geometric Priors

We present a novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, ours builds a set of candidate trees over many different subsets of points likely to belong to the optimal tree and then chooses the best one according to a global objective function that combines image evidence with geometric priors. Since the best tree does not necessarily span all the points, the algorithm is able to eliminate false detections while retaining the correct tree topology. Manually annotated brightfield micrographs, retinal scans and the DIADEM challenge datasets are used to evaluate the performance of our method. We used the DIADEM metric to quantitatively evaluate the topological accuracy of the reconstructions and showed that the use of the geometric regularization yields a substantial improvement

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

New strategies for row-crop management based on cost-effective remote sensors

Agricultural technology can be an excellent antidote to resource scarcity. Its growth has led to the extensive study of spatial and temporal in-field variability. The challenge of accurate management has been addressed in recent years through the use of accurate high-cost measurement instruments by researchers. However, low rates of technological adoption by farmers motivate the development of alternative technologies based on affordable sensors, in order to improve the sustainability of agricultural biosystems. This doctoral thesis has as main objective the development and evaluation of systems based on affordable sensors, in order to address two of the main aspects affecting the producers: the need of an accurate plant water status characterization to perform a proper irrigation management and the precise weed control. To address the first objective, two data acquisition methodologies based on aerial platforms have been developed, seeking to compare the use of infrared thermometry and thermal imaging to determine the water status of two most relevant row-crops in the region, sugar beet and super high-density olive orchards. From the data obtained, the use of an airborne low-cost infrared sensor to determine the canopy temperature has been validated. Also the reliability of sugar beet canopy temperature as an indicator its of water status has been confirmed. The empirical development of the Crop Water Stress Index (CWSI) has also been carried out from aerial thermal imaging combined with infrared temperature sensors and ground measurements of factors such as water potential or stomatal conductance, validating its usefulness as an indicator of water status in super high-density olive orchards. To contribute to the development of precise weed control systems, a system for detecting tomato plants and measuring the space between them has been developed, aiming to perform intra-row treatments in a localized and precise way. To this end, low cost optical sensors have been used and compared with a commercial LiDAR laser scanner. Correct detection results close to 95% show that the implementation of these sensors can lead to promising advances in the automation of weed control. The micro-level field data collected from the evaluated affordable sensors can help farmers to target operations precisely before plant stress sets in or weeds infestation occurs, paving the path to increase the adoption of Precision Agriculture techniques

Methods for Automated Neuron Image Analysis

Knowledge of neuronal cell morphology is essential for performing specialized analyses in the endeavor to understand neuron behavior and unravel the underlying principles of brain function. Neurons can be captured with a high level of detail using modern microscopes, but many neuroscientific studies require a more explicit and accessible representation than offered by the resulting images, underscoring the need for digital reconstruction of neuronal morphology from the images into a tree-like graph structure. This thesis proposes new computational methods for automated detection and reconstruction of neurons from fluorescence microscopy images. Specifically, the successive chapters describe and evaluate original solutions to problems such as the detection of landmarks (critical points) of the neuronal tree, complete tracing and reconstruction of the tree, and the detection of regions containing neurons in high-content screens