Computer-Aided Assessment of Tuberculosis with Radiological Imaging: From rule-based methods to Deep Learning

Mención Internacional en el título de doctorTuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb.) that produces pulmonary damage due to its airborne nature. This fact facilitates the disease fast-spreading, which, according to the World Health Organization (WHO), in 2021 caused 1.2 million deaths and 9.9 million new cases. Traditionally, TB has been considered a binary disease (latent/active) due to the limited specificity of the traditional diagnostic tests. Such a simple model causes difficulties in the longitudinal assessment of pulmonary affectation needed for the development of novel drugs and to control the spread of the disease. Fortunately, X-Ray Computed Tomography (CT) images enable capturing specific manifestations of TB that are undetectable using regular diagnostic tests, which suffer from limited specificity. In conventional workflows, expert radiologists inspect the CT images. However, this procedure is unfeasible to process the thousands of volume images belonging to the different TB animal models and humans required for a suitable (pre-)clinical trial. To achieve suitable results, automatization of different image analysis processes is a must to quantify TB. It is also advisable to measure the uncertainty associated with this process and model causal relationships between the specific mechanisms that characterize each animal model and its level of damage. Thus, in this thesis, we introduce a set of novel methods based on the state of the art Artificial Intelligence (AI) and Computer Vision (CV). Initially, we present an algorithm to assess Pathological Lung Segmentation (PLS) employing an unsupervised rule-based model which was traditionally considered a needed step before biomarker extraction. This procedure allows robust segmentation in a Mtb. infection model (Dice Similarity Coefficient, DSC, 94%±4%, Hausdorff Distance, HD, 8.64mm±7.36mm) of damaged lungs with lesions attached to the parenchyma and affected by respiratory movement artefacts. Next, a Gaussian Mixture Model ruled by an Expectation-Maximization (EM) algorithm is employed to automatically quantify the burden of Mtb.using biomarkers extracted from the segmented CT images. This approach achieves a strong correlation (R2 ≈ 0.8) between our automatic method and manual extraction. Consequently, Chapter 3 introduces a model to automate the identification of TB lesions and the characterization of disease progression. To this aim, the method employs the Statistical Region Merging algorithm to detect lesions subsequently characterized by texture features that feed a Random Forest (RF) estimator. The proposed procedure enables a selection of a simple but powerful model able to classify abnormal tissue. The latest works base their methodology on Deep Learning (DL). Chapter 4 extends the classification of TB lesions. Namely, we introduce a computational model to infer TB manifestations present in each lung lobe of CT scans by employing the associated radiologist reports as ground truth. We do so instead of using the classical manually delimited segmentation masks. The model adjusts the three-dimensional architecture, V-Net, to a multitask classification context in which loss function is weighted by homoscedastic uncertainty. Besides, the method employs Self-Normalizing Neural Networks (SNNs) for regularization. Our results are promising with a Root Mean Square Error of 1.14 in the number of nodules and F1-scores above 0.85 for the most prevalent TB lesions (i.e., conglomerations, cavitations, consolidations, trees in bud) when considering the whole lung. In Chapter 5, we present a DL model capable of extracting disentangled information from images of different animal models, as well as information of the mechanisms that generate the CT volumes. The method provides the segmentation mask of axial slices from three animal models of different species employing a single trained architecture. It also infers the level of TB damage and generates counterfactual images. So, with this methodology, we offer an alternative to promote generalization and explainable AI models. To sum up, the thesis presents a collection of valuable tools to automate the quantification of pathological lungs and moreover extend the methodology to provide more explainable results which are vital for drug development purposes. Chapter 6 elaborates on these conclusions.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidenta: María Jesús Ledesma Carbayo.- Secretario: David Expósito Singh.- Vocal: Clarisa Sánchez Gutiérre

Book of Abstracts

Book of Abstracts of the 5th European Turfgrass Society Conference, held in Salgados | Albufeira | Portugal. 6th - 8th, June, 2016.info:eu-repo/semantics/publishedVersio

Book of Abstracts

Book of Abstracts of the 5th European Turfgrass Society Conference, held in Salgados | Albufeira | Portugal. 6th - 8th, June, 2016.info:eu-repo/semantics/publishedVersio

Tightly-coupled manipulation pipelines: Combining traditional pipelines and end-to-end learning

Traditionally, robot manipulation tasks are solved by engineering solutions in a modular fashion --- typically consisting of object detection, pose estimation, grasp planning, motion planning, and finally run a control algorithm to execute the planned motion. This traditional approach to robot manipulation separates the hard problem of manipulation into several self-contained stages, which can be developed independently, and gives interpretable outputs at each stage of the pipeline. However, this approach comes with a plethora of issues, most notably, their generalisability to a broad range of tasks; it is common that as tasks get more difficult, the systems become increasingly complex. To combat the flaws of these systems, recent trends have seen robots visually learning to predict actions and grasp locations directly from sensor input in an end-to-end manner using deep neural networks, without the need to explicitly model the in-between modules. This thesis investigates a sample of methods, which fall somewhere on a spectrum from pipelined to fully end-to-end, which we believe to be more advantageous for developing a general manipulation system; one that could eventually be used in highly dynamic and unpredictable household environments. The investigation starts at the far end of the spectrum, where we explore learning an end-to-end controller in simulation and then transferring to the real world by employing domain randomisation, and finish on the other end, with a new pipeline, where the individual modules bear little resemblance to the "traditional" ones. The thesis concludes with a proposition of a new paradigm: Tightly-coupled Manipulation Pipelines (TMP). Rather than learning all modules implicitly in one large, end-to-end network or conversely, having individual, pre-defined modules that are developed independently, TMPs suggest taking the best of both world by tightly coupling actions to observations, whilst still maintaining structure via an undefined number of learned modules, which do not have to bear any resemblance to the modules seen in "traditional" systems.Open Acces

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Risks, impacts and management of invasive plant species in Vietnam

In Southeast Asia, research on invasive plant species (IPS) is limited and biased by geography, research foci and approaches. This may hinder understanding of the extent of invasion problems and effective management to prevent and control IPS. Because biological invasions are a complicated issue involving multiple disciplines, this thesis utilized diverse approaches to evaluate risk, impacts, and management of IPS in Vietnam. Distribution models of 14 species predicted that large areas of Vietnam are susceptible to IPS, particularly in parts bordering China. Native IPS, which are often overlooked in assessment, posed similar risks as non-native IPS. From the model results, a native grass Microstegium ciliatum was selected to quantify its impacts on tree regeneration in secondary forests. A field experiment in Cuc Phuong National Park found that tree seedling abundance and richness increased within one year of grass removal; this effect strengthened in the second year. These results highlight the impacts of IPS on tree regeneration and the importance of IPS management to forest restoration projects. Given the risks and impacts of IPS, strategic management is needed to achieve conservation goals in national parks (NPs). However, interviews with both state and non-state entities revealed poor and reactive management of IPS in Vietnamese NPs from national to local levels. Institutional arrangements challenge IPS management in Vietnam. Involvement of multiple sectors with unclear mandates leads to overlaps in responsibilities and makes collaboration among sectors difficult. Lack of top-down support from the national level (legislation, guidance, resources) and limited power at the local level weakens implementation and ability of NPs to respond to IPS. The findings of this thesis provide important information for achieving effective management of IPS in Vietnam. Knowledge of vulnerable areas and species likely to invade and cause impacts can help Vietnam efficiently allocate management resources to prevent and control IPS, but adjustments to institutional arrangements and enhanced cooperation may be necessary to ensure management occurs

Root trait variation and its contribution to drought tolerance in bambara groundnut (Vigna subterranea (L.) Verdc.)

Bambara groundnut (Vigna subterranea (L) Verdc), is an exemplar neglected African grain legume that thrives under strikingly contrasted environments relative to other grain legumes. Originating in West Africa, its distribution spans across aridity gradients from tropical dry climates in Senegal and Kenya, respectively, down to arid and semi-arid regions in sub-Saharan Africa. This is on soils more or less poor in nutrients and formed under variable pedoclimatic conditions. In these contrasting habitats, it is generally agreed that bambara groundnut has diversified due to domestication from its wild relative, Vigna subterranea var. spontanea (Harms) Hepper, as a result of steady changes through natural and artificial selection. Bambara groundnut is a close relative of cowpea (Vigna unguiculata) and morphologically fits into the same niche as groundnut (Arachis hypogaea L.). The wide distribution in natural environments and ability to tolerate both biotic and abiotic stresses better than cowpea and groundnut, make bambara groundnut an interesting model for examining diversification in response to ephemeral soil water resources. Although important, comprehensive variation assessment on below ground (root) traits in bambara groundnut have not been pursued. The hypothesis was that by focusing on naturally occurring genotypic variation in root system architecture and rooting distribution, bambara groundnut genotypes from dry agroecologies with periodic drought stress had developed root system traits that improved water foraging in deeper soil depths over time. This could be visualised and quantified using a low-cost polyvinyl chloride column (PVC) phenotyping system and image analysis. To test this hypothesis, morphological variability in root system architecture was characterized in eight bambara groundnut parental lines of varying geographic origin (Gresik, LunT, IITA-686, DodR, S19-3, Tiga nicuru, Ankpa-4, DipC1). The experiment was conducted over two seasons (2018 and 2019) under fixed rainout shelter at the Crops For the Future-Field Research Center (CFF-FRC) in Semenyih, Malaysia. Results revealed that in deeper (60-90cm) soil depths, genotypes S19-3 and DipC1 from drier regions of Sub-Saharan Africa had longer tap roots and greater root length distribution. Bambara groundnut genotypes from wetter regions in Southeast Asia and West Africa (i.e., Gresik, LunT, and IITA-686), on the other hand, had shallower and more branched root growth closer to the soil surface. Genotypes generally displayed two extremes in root foraging patterns and branching habits i.e., deep-cheap rooting in genotypes sourced from dry regions and shallow-costly rooting in genotypes adapted to higher rainfall areas with shallow soils. Next, the natural genotypic diversity revealed in the eight genotypes was then investigated to detect adaptive changes in tap root length and root length density in response to periodic drought stress. Genotypes were grown in PVC columns in well-watered and 30-day drought stress (DS) treatments for two seasons (2018 and 2019). DS significantly (P < 0.05 - < 0.001) reduced average shoot height, number of leaves, and delayed flowering in 2018 and 2019. In 2018, the average root-to-shoot ratio was significantly higher (P < 0.001; 22%) under DS treatment. On average tap root length at 55 days after emergence (DAE) i.e., end of 30-d DS, was reduced by 14% and 22% in 2018 and 2019, respectively, and by 5% and 11% at 105 DAE (50-days of DS recovery) in 2018 and 2019, respectively, with some genotypes in 2018: DodR (55 DAE); LunT and Ankpa-4 (105 DAE) and in 2019: IITA-686 (105 DAE), increasing to measurements comparable to the well-watered (WW) treatment. In 2018 and 2019, root length density in the DS treatment was associated with significant grain yield advantage (R2 = 0.27 and R2 = 0.49) in 2018 and 2019, respectively. This indicates that the various agroecological conditions to which bambara groundnut has been exposed in its natural setting may have induced phenotypic differentiation to adapt to ecotypic conditions, which may help offset the impact of adverse events like regular drought stress. When looking for superior genotypes, ecotypic distinction can be an interesting aspect to remember. Finally, root traits such as tap root length and root length density in the 60-90cm soil layer were shown to be beneficial in screening and selecting superior lines from a bambara groundnut population. The population was derived from a cross between two parental lines i.e., S19-3 (maternal) × DodR (paternal). Across replicates, 100-seed weight had the lowest average repeatability (0.62), while high repeatability values were observed for root length density in the 60-90cm soil depth (0.99). Under DS environment (50-d of DS recovery), root length density in the 60-90cm soil depth was strongly correlated (P < 0.05 - P < 0.001) with shoot traits such as number of leaves (r = 0.69), shoot dry weight (r = 0.78), and shoot height (r = 0.67). This indicates that shoot traits are useful traits that can also be used as proxies to make estimations of root length density. According to a regression analysis, root length density in the 60-90cm soil depth was associated with grain yield (R2 = 42%; P < 0.001). According to biplot analysis, the top three bambara groundnut lines in terms of yield under drought stress were ‘Line12′, ‘Line35′, and ‘Line41′. Overall, the work provides a novel and in-depth examination of bambara groundnut below-ground (root trait variation) and its role to drought tolerance. According to this research, bambara groundnut possess differential deep root foraging and density patterns with two extremes i.e., deep-cheap rooting in the genotypes mainly sourced from dry regions and a shallow-costly rooting system in genotypes suited to higher rainfall areas. Farmers have inadvertently selected for these variations over time due to their effect on yield in both dry and wet conditions. Drought tolerance breeding for bambara groundnut will more likely accelerate as a consequence of a better understanding of root systems and foraging patterns. Selected high yielding lines from the S19-3 (maternal) × DodR (paternal) cross i.e., ‘Line12′, ‘Line35′ and ‘Line41′ — all exhibiting deep and extensive rooting in deeper soil depths, will be advanced as part of the current Future Food Beacon: Bambara Groundnut breeding (BamBREED) research project. Elite lines generated from this breeding programme could be registered as improved varieties and released to the general public for cultivation in drought-prone areas. This is projected to boost dietary diversity and significantly increase the nutritional value of people's diets