Morphological Network: How Far Can We Go with Morphological Neurons?

In recent years, the idea of using morphological operations as networks has received much attention. Mathematical morphology provides very efficient and useful image processing and image analysis tools based on basic operators like dilation and erosion, defined in terms of kernels. Many other morphological operations are built up using the dilation and erosion operations. Although the learning of structuring elements such as dilation or erosion using the backpropagation algorithm is not new, the order and the way these morphological operations are used is not standard. In this paper, we have theoretically analyzed the use of morphological operations for processing 1D feature vectors and shown that this gets extended to the 2D case in a simple manner. Our theoretical results show that a morphological block represents a sum of hinge functions. Hinge functions are used in many places for classification and regression tasks (Breiman (1993)). We have also proved a universal approximation theorem -- a stack of two morphological blocks can approximate any continuous function over arbitrary compact sets. To experimentally validate the efficacy of this network in real-life applications, we have evaluated its performance on satellite image classification datasets since morphological operations are very sensitive to geometrical shapes and structures. We have also shown results on a few tasks like segmentation of blood vessels from fundus images, segmentation of lungs from chest x-ray and image dehazing. The results are encouraging and further establishes the potential of morphological networks.Comment: 35 pages, 19 figures, 7 table

Convergence among Non-Sister Dendritic Branches: An Activity-Controlled Mean to Strengthen Network Connectivity

The manner by which axons distribute synaptic connections along dendrites remains a fundamental unresolved issue in neuronal development and physiology. We found in vitro and in vivo indications that dendrites determine the density, location and strength of their synaptic inputs by controlling the distance of their branches from those of their neighbors. Such control occurs through collective branch convergence, a behavior promoted by AMPA and NMDA glutamate receptor activity. At hubs of convergence sites, the incidence of axo-dendritic contacts as well as clustering levels, pre- and post-synaptic protein content and secretion capacity of synaptic connections are higher than found elsewhere. This coupling between synaptic distribution and the pattern of dendritic overlapping results in ‘Economical Small World Network’, a network configuration that enables single axons to innervate multiple and remote dendrites using short wiring lengths. Thus, activity-mediated regulation of the proximity among dendritic branches serves to pattern and strengthen neuronal connectivity

3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries

Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.Comment: 39 pages, 14 figures. High resolution figures and supplemental movies available upon reques

Artificial immune systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm

Modeling dendritic shapes - using path planning

Dendritic shapes are commonplace in the natural world such as trees, lichens, coral and lightning. Models of dendritic shapes are widely needed in many areas. Because of their branching fractal and erratic structures modeling dendritic shapes is a tricky task. Existing methods for modeling dendritic shapes are slow and complicated.In this thesis we present a procedural algorithm of using path planning to model dendritic shapes. We generate a dendrite by finding the least-cost paths from multiple endpoints to a common generator and use the dendrite to build the geometric model. With the control handles of endpoint placement, fractal shape, edge weights distribution and path width, we create different shapes of dendrites that simulate different kinds of dendritic shapes very well. Compared with some existing methods, our algorithm is fast and simple

Apprentissage faiblement supervisé appliqué à la segmentation d'images de protéines neuronales

Titre de l'écran-titre (visionné le 9 juillet 2020)Thèse ou mémoire avec insertion d'articlesTableau d'honneur de la Faculté des études supérieures et postdoctorales, 2020-2021En biologie cellulaire, la microscopie optique est couramment utilisée pour visualiser et caractériser la présence et la morphologie des structures biologiques. Suite à l’acquisition, un expert devra effectuer l’annotation des structures pour quantification. Cette tâche est ardue, requiert de nombreuses heures de travail, parfois répétitif, qui peut résulter en erreurs d’annotations causées par la fatigue d’étiquetage. L’apprentissage machine promet l’automatisation de tâches complexes à partir d’un grand lot de données exemples annotés. Mon projet de maîtrise propose d’utiliser des techniques faiblement supervisées, où les annotations requises pour l’entraînement sont réduites et/ou moins précises, pour la segmentation de structures neuronales. J’ai d’abord testé l’utilisation de polygones délimitant la structure d’intérêt pour la tâche complexe de segmentation de la protéine neuronale F-actine dans des images de microscopie à super-résolution. La complexité de la tâche est supportée par la morphologie hétérogène des neurones, le nombre élevé d’instances à segmenter dans une image et la présence de nombreux distracteurs. Malgré ces difficultés, l’utilisation d’annotations faibles a permis de quantifier un changement novateur de la conformation de la protéine F-actine en fonction de l’activité neuronale. J’ai simplifié davantage la tâche d’annotation en requérant seulement des étiquettes binaires renseignant sur la présence des structures dans l’image réduisant d’un facteur 30 le temps d’annotation. De cette façon, l’algorithme est entraîné à prédire le contenu d’une image et extrait ensuite les caractéristiques sémantiques importantes pour la reconnaissance de la structure d’intérêt à l’aide de mécanismes d’attention. La précision de segmentation obtenue sur les images de F-actine est supérieure à celle des annotations polygonales et équivalente à celle des annotations précises d’un expert. Cette nouvelle approche devrait faciliter la quantification des changements dynamiques qui se produisent sous le microscope dans des cellules vivantes et réduire les erreurs causées par l’inattention ou le biais de sélection des régions d’intérêt dans les images de microscopie.In cell biology, optical microscopy is commonly used to visualize and characterize the presenceand morphology of biological structures. Following the acquisition, an expert will have toannotate the structures for quantification. This is a difficult task, requiring many hours ofwork, sometimes repetitive, which can result in annotation errors caused by labelling fatigue.Machine learning promises to automate complex tasks from a large set of annotated sampledata. My master’s project consists of using weakly supervised techniques, where the anno-tations required for training are reduced and/or less precise, for the segmentation of neuralstructures.I first tested the use of polygons delimiting the structure of interest for the complex taskof segmentation of the neuronal protein F-actin in super-resolution microscopy images. Thecomplexity of the task is supported by the heterogeneous morphology of neurons, the highnumber of instances to segment in an image and the presence of many distractors. Despitethese difficulties, the use of weak annotations has made it possible to quantify an innovativechange in the conformation of the F-actin protein as a function of neuronal activity. I furthersimplified the annotation task by requiring only binary labels that indicate the presence ofstructures in the image, reducing annotation time by a factor of 30. In this way, the algorithmis trained to predict the content of an image and then extract the semantic characteristicsimportant for recognizing the structure of interest using attention mechanisms. The segmen-tation accuracy obtained on F-actin images is higher than that of polygonal annotations andequivalent to that of an expert’s precise annotations. This new approach should facilitate thequantification of dynamic changes that occur under the microscope in living cells and reduceerrors caused by inattention or bias in the selection of regions of interest in microscopy images