Susana Mata

This study presents a tool, Neuronize, for building realistic three-dimensional models of neurona... more This study presents a tool, Neuronize, for building realistic three-dimensional models of neuronal cells from the morphological information extracted through computer-aided tracing applications. Neuronize consists of a set of methods designed to build 3D neural meshes that approximate the cell membrane at different resolution levels, allowing a balance to be reached between the complexity and the quality of the final model. The main contribution of the present study is the proposal of a novel approach to build a realistic and accurate 3D shape of the soma from the incomplete information stored in the digitally traced neuron, which usually consists of a 2D cell body contour. This technique is based on the deformation of an initial shape driven by the position and thickness of the first order dendrites. The addition of a set of spines along the dendrites completes the model, building a final 3D neuronal cell suitable for its visualization in a wide range of 3D environments.

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Carbonic: A Framework for Creating and Visualizing Complex Compound Graphs

Applied Sciences

Advances in data generation and acquisition have resulted in a volume of available data of such m... more Advances in data generation and acquisition have resulted in a volume of available data of such magnitude that our ability to interpret and extract valuable knowledge from them has been surpassed. Our capacity to analyze data is hampered not only by their amount or their dimensionality, but also by their relationships and by the complexity of the systems they model. Compound graphs allow us to represent the existing relationships between nodes that are themselves hierarchically structured, so they are a natural substrate to support multiscale analysis of complex graphs. This paper presents Carbonic, a framework for interactive multiscale visual exploration and editing of compound graphs that incorporates several strategies for complexity management. It combines the representation of graphs at multiple levels of abstraction, with techniques for reducing the number of visible elements and for reducing visual cluttering. This results in a tool that allows both the exploration of existi...

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Mesh Simplification Using Distance Labels for View-Independent Silhouette Preservation

International Conference on Computer Graphics Theory and Applications, 2008

Alluvial fan at Coprates Catena in Valles Marineris, Mars: new modeling insights

A Unified Framework for Neuroscience Morphological Data Visualization

Applied Sciences, 2021

Funding: The research leading to these results received funding from the Spanish Ministry of Econ... more Funding: The research leading to these results received funding from the Spanish Ministry of Economy and Competitiveness under grants C080020-09 (Cajal Blue Brain Project, Spanish partner of the Blue Brain Project initiative from EPFL) and TIN2017-83132, as well as from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements No. 785907 (Human Brain Project SGA2) and 945539 (Human Brain Project SGA3)

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NeuroScheme: Efficient Multiscale Representations for the Visual Exploration of Morphological Data in the Human Brain Neocortex

The analysis of data sets from such a complex domain as neuroscience is severely hampered by the ... more The analysis of data sets from such a complex domain as neuroscience is severely hampered by the brain complexity and by the huge amount of data originated in current experiments and simulations. The complexity of the brain stems not only from the enormous number of relevant entities that integrate it (for example, around 10<sup>15 </sup>synapses), but also from the complex interrelations among brain structures, which have to be analyzed at the many different organizational levels of the brain. Visualization techniques have proven to be valuable in order to explore and analyze complex systems from a wide variety of areas. Nevertheless, their application to the field of neuroscience must face some domain specific difficulties. In particular, the overwhelming amount of data poses a challenge to the current storage and processing capabilities, calling for compact data descriptions. Furthermore, the visual analysis of neural scenes requires the design of novel visual representations and techniques that help experts in their data exploration and interpretation activities, assisting them in fighting complexity. In this paper we propose a visual exploratory framework that facilitates the process of knowledge extraction from complex neural scenes. This framework contains a multilevel structure, following the different organizational levels of the brain. Schematic or iconic symbols have been designed to portray the entities at each level, providing graphical representations that emphasize relevant features while hiding less important information. These schematic views, together with a multilevel organization, allow the exploration of the brain at different scales, combining in the same view different levels of abstraction whose entities can be either schematically represented (at different abstraction levels) or geometrically depicted at the finest level of detail. The fact that users can choose high geometric detail for just a fraction of brain structures results in a dramatic reduction of visual clutter, dec [...]

Journal of Virtual Reality and Broadcasting, Volume 3(2006), no. 4 View-Dependent Extraction of Contours with Distance Transforms for Adaptive Polygonal Mesh Simplification Abstract

www: www.dtf.fi.upm.es/˜arodri During decades Distance Transforms have proven to be useful for ma... more www: www.dtf.fi.upm.es/˜arodri During decades Distance Transforms have proven to be useful for many image processing applications, and more recently, they have started to be used in computer graphics environments. The goal of this paper is to propose a new technique based on Distance Transforms for detecting mesh elements which are close to the objects ’ external contour (from a given point of Digital Peer Publishing Licence Any party may pass on this Work by electronic means and make it available for download under the terms and conditions of the current version of the Digital Peer Publishing Licence (DPPL). The text of the licence may be accessed and retrieved via Internet at

Visual exploration and generation of connectivity in neural networks, bridging the gap between empirical data and theoretical model definition

Influence of Location over Several Classifiers in 2D and 3D Face Verification

In this paper two methods for human face recognition and the influence of location mistakes are s... more In this paper two methods for human face recognition and the influence of location mistakes are shown. First one, Principal Components Analysis (PCA), has been one of the most applied methods to perform face verification in 2D. In our experiments three classifiers have been considered to test influence of location errors in face verification using PCA. An initial set of "correct located faces" has been used for PCA matrix computation and to train all classifiers. An initial test set was built considering a "correct located faces" set (based on different images than training ones) and then a new test set was obtained by applying a small displacement in both axis (20 pixels) to the initial set. Second method is based on geometrical characteristics constructed with facial and cranial points that come from a 3D representation. Data are acquired by a calibrated stereo system. Classifiers considered for both methods are knearest neighbours (KNN), artificial neural networks: radial basis function (RBF) and Support Vector Machine (SVM). Given our data set, results show that SVM is capable to classify correctly in the presence of small location errors. RBF has an acceptable correct rate but the number of false positives is always higher than in the SVM case.

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ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks

Frontiers in Neuroinformatics

An open challenge on the road to unraveling the brain's multilevel organization is establishi... more An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels—ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce ...

View-Dependent Extraction of Contours with Distance Transforms for Adaptive Polygonal for adaptive polygonal Mesh-Simplification

JVRB, 3(2006), no. 4. - During decades Distance Transforms have proven to be useful for many imag... more JVRB, 3(2006), no. 4. - During decades Distance Transforms have proven to be useful for many image processing applications, and more recently, they have started to be used in computer graphics environments. The goal of this paper is to propose a new technique based on Distance Transforms for detecting mesh elements which are close to the objects' external contour (from a given point of view), and using this information for weighting the approximation error which will be tolerated during the mesh simplification process. The obtained results are evaluated in two ways: visually and using an objective metric that measures the geometrical difference between two polygonal meshes.

Neuronize v2: Bridging the Gap Between Existing Proprietary Tools to Optimize Neuroscientific Workflows

Frontiers in Neuroanatomy, 2020

Knowledge about neuron morphology is key to understanding brain structure and function. There are... more Knowledge about neuron morphology is key to understanding brain structure and function. There are a variety of software tools that are used to segment and trace the neuron morphology. However, these tools usually utilize proprietary formats. This causes interoperability problems since the information extracted with one tool cannot be used in other tools. This article aims to improve neuronal reconstruction workflows by facilitating the interoperability between two of the most commonly used software tools—Neurolucida (NL) and Imaris (Filament Tracer). The new functionality has been included in an existing tool—Neuronize—giving rise to its second version. Neuronize v2 makes it possible to automatically use the data extracted with Imaris Filament Tracer to generate a tracing with dendritic spine information that can be read directly by NL. It also includes some other new features, such as the ability to unify and/or correct inaccurately-formed meshes (i.e., dendritic spines) and to cal...

Visualización de datos multidimensionales y multivariables en entornos tridimensionales, aplicados a la ingeniería civil

En el presente proyecto, se ha creado una aplicacion que reunira toda la informacion recopilada d... more En el presente proyecto, se ha creado una aplicacion que reunira toda la informacion recopilada de distintas investigaciones realizadas en el campo de la visualizacion de datos, que posteriormente permitira simular el desplazamiento de las capas superficiales y el terreno 6 que se encuentra a cierta profundidad. Dicha aplicacion, constituye una herramienta auxiliar en el estudio geotecnico antes de proceder a excavar, para elegir los emplazamientos adecuados para los cimientos y puntos fijos que necesita una estructura importante, y sobretodo que sea segura y duradera.

View-Dependent Extraction of Contours with Distance Transforms for adaptive polygonal Mesh-Simplification

J. Virtual Real. Broadcast., 2006

During decades Distance Transforms have proven to be useful for many image processing application... more During decades Distance Transforms have proven to be useful for many image processing applications, and more recently, they have started to be used in computer graphics environments. The goal of this paper is to propose a new technique based on Distance Transforms for detecting mesh elements which are close to the objects’ external contour (from a given point of

Non-Photorealistic Rendering of Neural Cells from their Morphological Description

J. Univers. Comput. Sci., 2015

Gaining a better understanding of the human brain continues to be one of the great- est and most ... more Gaining a better understanding of the human brain continues to be one of the great- est and most elusive of challenges. Its extreme complexity can only be addressed through the coordinated and collaborative work of researchers from a range of disciplines. 3D visualization has proven to be a useful tool for simplifying the analysis of complex systems, where gaining meaningful understanding from unstructured raw data is almost impossible, such as in the case of the brain. This paper presents a novel approach for visualizing neurons directly from the mor- phological descriptions extracted by neuroscience laboratories, pursuing two goals: improving the readability of complex neuronal scenarios and avoiding the need to store 3D models of the intricate geometry of neurons, since such models are demanding of computer resources. The proposed rendering method involves illustration techniques that facilitate the visual analysis of dense neural scenes. The work presented here brings the field ...

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A Method for the Symbolic Representation of Neurons

Frontiers in Neuroanatomy

The field of neuroanatomy has progressed considerably in recent decades, thanks to the emergence ... more The field of neuroanatomy has progressed considerably in recent decades, thanks to the emergence of novel methods which provide new insights into the organization of the nervous system. These new methods have produced a wealth of data that needs to be analyzed, shifting the bottleneck from the acquisition to the analysis of data. In other disciplines, such as in many engineering areas, scientists and engineers are dealing with increasingly complex systems, using hierarchical decompositions, graphical models and simplified schematic diagrams for analysis and design processes. This approach makes it possible for users to simultaneously combine global system views and very detailed representations of specific areas of interest, by selecting appropriate representations for each of these views. In this way, users can concentrate on specific details while also maintaining a general system overview-a capability that is essential for understanding structure and function whenever complexity is an issue. Following this approach, this paper focuses on a graphical tool designed to help neuroanatomists to better understand and detect morphological characteristics of neuronal cells. The method presented here, based on a symbolic representation that can be tailored to enhance a particular range of features of a neuron or neuron set, has proven to be useful for highlighting particular geometries that may be hidden due to the complexity of the analysis tasks and the richness of neuronal morphologies. A software tool has been developed to generate graphical representations of neurons from 3D computer-aided reconstruction files.

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Combining visual relevance criteria into adaptive mesh simplification techniques

2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, 2009

ABSTRACT A wide range of simplification techniques for 3D meshes may take advantage of the limita... more ABSTRACT A wide range of simplification techniques for 3D meshes may take advantage of the limitations of the human visual system by concentrating computational resources in those regions where the user&#39;s attention will focus most often. Some of those visually outstanding regions may be extracted by means of an automatic visual attention model. Additionally, there are some features that are known to have a high impact in the quality perceived by our visual system, as for example the silhouettes. The goal of this paper is to present the feasibility of combining different perceptual criteria into a set of labels that encode the importance of mesh elements according to their distance to relevant regions. These regions may have been independently extracted by applying different criteria in preprocessing time. The combination of the perceptual criteria is parameterizable, allowing to vary their relative importance. Moreover, the possibility of performing this combination in real time allows to adapt the regions of attention to the visualization conditions.

Mesh Simplification Using Distance Labels for View-Independent Silhouette Preservation

Grapp, 2008

Visualización de datos multidimensionales y multivariables en entornos tridimensionales, aplicados a la ingeniería civil

Estudio de la generación del soma en función de métodos de deformación

Neuronize: a tool for building realistic neuronal cell morphologies

Frontiers in Neuroanatomy, 2013

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Carbonic: A Framework for Creating and Visualizing Complex Compound Graphs

Applied Sciences

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Mesh Simplification Using Distance Labels for View-Independent Silhouette Preservation

International Conference on Computer Graphics Theory and Applications, 2008

Alluvial fan at Coprates Catena in Valles Marineris, Mars: new modeling insights

A Unified Framework for Neuroscience Morphological Data Visualization

Applied Sciences, 2021

Download

NeuroScheme: Efficient Multiscale Representations for the Visual Exploration of Morphological Data in the Human Brain Neocortex

Journal of Virtual Reality and Broadcasting, Volume 3(2006), no. 4 View-Dependent Extraction of Contours with Distance Transforms for Adaptive Polygonal Mesh Simplification Abstract

Visual exploration and generation of connectivity in neural networks, bridging the gap between empirical data and theoretical model definition

Influence of Location over Several Classifiers in 2D and 3D Face Verification

Download

ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks

Frontiers in Neuroinformatics

View-Dependent Extraction of Contours with Distance Transforms for Adaptive Polygonal for adaptive polygonal Mesh-Simplification

Neuronize v2: Bridging the Gap Between Existing Proprietary Tools to Optimize Neuroscientific Workflows

Frontiers in Neuroanatomy, 2020

Visualización de datos multidimensionales y multivariables en entornos tridimensionales, aplicados a la ingeniería civil

View-Dependent Extraction of Contours with Distance Transforms for adaptive polygonal Mesh-Simplification

J. Virtual Real. Broadcast., 2006

Non-Photorealistic Rendering of Neural Cells from their Morphological Description

J. Univers. Comput. Sci., 2015

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A Method for the Symbolic Representation of Neurons

Frontiers in Neuroanatomy

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Combining visual relevance criteria into adaptive mesh simplification techniques

2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, 2009

Mesh Simplification Using Distance Labels for View-Independent Silhouette Preservation

Grapp, 2008

Visualización de datos multidimensionales y multivariables en entornos tridimensionales, aplicados a la ingeniería civil

Estudio de la generación del soma en función de métodos de deformación

Susana Mata

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