73 research outputs found

    Deep learning for bioimage analysis in developmental biology

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    Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time

    Understanding Vehicular Traffic Behavior from Video: A Survey of Unsupervised Approaches

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    Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provide

    The ant abdomen: The skeletomuscular and soft tissue anatomy of Amblyopone australis workers (Hymenoptera: Formicidae)

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    Recent studies of insect anatomy evince a trend towards a comprehensive and integrative investigation of individual traits and their evolutionary relationships. The abdomen of ants, however, remains critically understudied. To address this shortcoming, we describe the abdominal anatomy of Amblyopone australis Erichson, using a multimodal approach combining manual dissection, histology, and microcomputed tomography. We focus on skeletomusculature, but additionally describe the metapleural and metasomal exocrine glands, and the morphology of the circulatory, digestive, reproductive, and nervous systems. We describe the muscles of the dorsal vessel and the ducts of the venom and Dufour\u27s gland, and characterize the visceral anal musculature. Through comparison with other major ant lineages, apoid wasps, and other hymenopteran outgroups, we provide a first approximation of the complete abdominal skeletomuscular groundplan in Formicidae, with a nomenclatural schema generally applicable to the hexapod abdomen. All skeletal muscles were identifiable with their homologs, while we observe potential apomorphies in the pregenital skeleton and the sting musculature. Specifically, we propose the eighth coxocoxal muscle as an ant synapomorphy; we consider possible transformation series contributing to the distribution of states of the sternal apodemes in ants, Hymenoptera, and Hexapoda; and we address the possibly synapomorphic loss of the seventh sternal–eighth gonapophyseal muscles in the vespiform Aculeata. We homologize the ovipositor muscles across Hymenoptera, and summarize demonstrated and hypothetical muscle functions across the abdomen. We also give a new interpretation of the proximal processes of gonapophyses VIII and the ventromedial processes of gonocoxites IX, and make nomenclatural suggestions in the context of evolutionary anatomy and ontology. Finally, we discuss the utility of techniques applied and emphasize the value of primary anatomical research

    Reconstruction robuste de formes à partir de données imparfaites

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    Over the last two decades, a high number of reliable algorithms for surface reconstruction from point clouds has been developed. However, they often require additional attributes such as normals or visibility, and robustness to defect-laden data is often achieved through strong assumptions and remains a scientific challenge. In this thesis we focus on defect-laden, unoriented point clouds and contribute two new reconstruction methods designed for two specific classes of output surfaces. The first method is noise-adaptive and specialized to smooth, closed shapes. It takes as input a point cloud with variable noise and outliers, and comprises three main steps. First, we compute a novel noise-adaptive distance function to the inferred shape, which relies on the assumption that this shape is a smooth submanifold of known dimension. Second, we estimate the sign and confidence of the function at a set of seed points, through minimizing a quadratic energy expressed on the edges of a uniform random graph. Third, we compute a signed implicit function through a random walker approach with soft constraints chosen as the most confident seed points. The second method generates piecewise-planar surfaces, possibly non-manifold, represented by low complexity triangle surface meshes. Through multiscale region growing of Hausdorff-error-bounded convex planar primitives, we infer both shape and connectivity of the input and generate a simplicial complex that efficiently captures large flat regions as well as small features and boundaries. Imposing convexity of primitives is shown to be crucial to both the robustness and efficacy of our approach.Au cours des vingt dernières années, de nombreux algorithmes de reconstruction de surface ont été développés. Néanmoins, des données additionnelles telles que les normales orientées sont souvent requises et la robustesse aux données imparfaites est encore un vrai défi. Dans cette thèse, nous traitons de nuages de points non-orientés et imparfaits, et proposons deux nouvelles méthodes gérant deux différents types de surfaces. La première méthode, adaptée au bruit, s'applique aux surfaces lisses et fermées. Elle prend en entrée un nuage de points avec du bruit variable et des données aberrantes, et comporte trois grandes étapes. Premièrement, en supposant que la surface est lisse et de dimension connue, nous calculons une fonction distance adaptée au bruit. Puis nous estimons le signe et l'incertitude de la fonction sur un ensemble de points-sources, en minimisant une énergie quadratique exprimée sur les arêtes d'un graphe uniforme aléatoire. Enfin, nous calculons une fonction implicite signée par une approche dite « random walker » avec des contraintes molles choisies aux points-sources de faible incertitude. La seconde méthode génère des surfaces planaires par morceaux, potentiellement non-variétés, représentées par des maillages triangulaires simples. En faisant croitre des primitives planaires convexes sous une erreur de Hausdorff bornée, nous déduisons à la fois la surface et sa connectivité et générons un complexe simplicial qui représente efficacement les grandes régions planaires, les petits éléments et les bords. La convexité des primitives est essentielle pour la robustesse et l'efficacité de notre approche

    Generative Interpretation of Medical Images

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    Computational Methods for Modelling and Analysing Biological Networks

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    The main theme of this thesis is modelling and analysis of biological networks. Measurement data from biological systems is being produced at such a pace that it is impossible to make use of it without computational models and inference algorithms. The methods and models presented here aim at allowing to extract relevant relationships from the masses of data and formulating complex biological hypotheses that can be studied via simulation. The problem of learning the structure of a popular method class, Bayesian networks, from measurement data is investigated in this thesis, and an improvement to the standard method is presented that facilitates finding the correct network structure. Furthermore, this thesis studies active learning, where the structure inference algorithm can itself suggest measurements to be made. Active learning is applied to realistic scenarios with measured datasets and an active learning method that can deal with heterogeneous data types is presented. Another focus of this thesis is on analysing networks whose structure is known. The utility of a standard method for selecting beneficial mutations in metabolic networks is evaluated in the context of engineering the network to produce a desired substance at a higher rate than normally. Metabolic network modelling is also used in conjunction with a simulation of a biochemical network controlling bacterial movement in a state-based and executable framework that can integrate different submodels. This combined model is then used to simulate the behaviour of a population of bacteria. In summary, this thesis presents improvements on methods for learning network structures, evaluates the utility of an analysis method for identifying suitable mutations for producing a substance of interest, and introduces a state-based modelling framework capable of integrating several submodels

    Doctor of Philosophy

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    dissertationThe medial axis of an object is a shape descriptor that intuitively presents the morphology or structure of the object as well as intrinsic geometric properties of the object’s shape. These properties have made the medial axis a vital ingredient for shape analysis applications, and therefore the computation of which is a fundamental problem in computational geometry. This dissertation presents new methods for accurately computing the 2D medial axis of planar objects bounded by B-spline curves, and the 3D medial axis of objects bounded by B-spline surfaces. The proposed methods for the 3D case are the first techniques that automatically compute the complete medial axis along with its topological structure directly from smooth boundary representations. Our approach is based on the eikonal (grassfire) flow where the boundary is offset along the inward normal direction. As the boundary deforms, different regions start intersecting with each other to create the medial axis. In the generic situation, the (self-) intersection set is born at certain creation-type transition points, then grows and undergoes intermediate transitions at special isolated points, and finally ends at annihilation-type transition points. The intersection set evolves smoothly in between transition points. Our approach first computes and classifies all types of transition points. The medial axis is then computed as a time trace of the evolving intersection set of the boundary using theoretically derived evolution vector fields. This dynamic approach enables accurate tracking of elements of the medial axis as they evolve and thus also enables computation of topological structure of the solution. Accurate computation of geometry and topology of 3D medial axes enables a new graph-theoretic method for shape analysis of objects represented with B-spline surfaces. Structural components are computed via the cycle basis of the graph representing the 1-complex of a 3D medial axis. This enables medial axis based surface segmentation, and structure based surface region selection and modification. We also present a new approach for structural analysis of 3D objects based on scalar functions defined on their surfaces. This approach is enabled by accurate computation of geometry and structure of 2D medial axes of level sets of the scalar functions. Edge curves of the 3D medial axis correspond to a subset of ridges on the bounding surfaces. Ridges are extremal curves of principal curvatures on a surface indicating salient intrinsic features of its shape, and hence are of particular interest as tools for shape analysis. This dissertation presents a new algorithm for accurately extracting all ridges directly from B-spline surfaces. The proposed technique is also extended to accurately extract ridges from isosurfaces of volumetric data using smooth implicit B-spline representations. Accurate ridge curves enable new higher-order methods for surface analysis. We present a new definition of salient regions in order to capture geometrically significant surface regions in the neighborhood of ridges as well as to identify salient segments of ridges
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