Repozytorium obrazów na potrzeby Inżynierii odwrotnej i symulacji medycznej

Novel technologies such as 3D printing (additive manufacturing), 3D scanning and reverse engineering may significantly improve application of the principles of medicine in current clinical practice. This paper aims at presentation of the own concept of the repository of medical images based on 3D printing and reverse engineering technology. The proposed concept of the repository can constitute a beginning of the novel family of commercial techniques needed for development and optimization of reverse engineering toward printing the fully clinically functional solutions.Nowe technologie, takie jak druk 3D, skanowanie 3D czy inżynieria odwrotna, mogą znacząco poprawić stosowanie reguł medycyny w obecnej praktyce klinicznej. Artykuł prezentuje własną koncepcję repozytorium obrazów medycznych w oparciu o technologie druku 3D i inżynierii odwrotnej. Proponowana koncepcja może stanowić początek nowej rodziny technik komercyjnych potrzebnych do rozwoju i optymalizacji inżynierii odwrotnej w kierunku drukowanie rozwiązań w pełni funkcjonalnych klinicznie

Repository of images for reverse engineering and medical simulation purposes

Novel technologies such as 3D printing (additive manufacturing), 3D scanning and reverse engineering may significantly improve application of the principles of medicine in current clinical practice. This paper aims at presentation of the own concept of the repository of medical images based on 3D printing and reverse engineering technology. The proposed concept of the repository can constitute a beginning of the novel family of commercial techniques needed for development and optimization of reverse engineering toward printing the fully clinically functional solutions.Nowe technologie, takie jak druk 3D, skanowanie 3D czy inżynieria odwrotna, mogą znacząco poprawić stosowanie reguł medycyny w obecnej praktyce klinicznej. Artykuł prezentuje własną koncepcję repozytorium obrazów medycznych w oparciu o technologie druku 3D i inżynierii odwrotnej. Proponowana koncepcja może stanowić początek nowej rodziny technik komercyjnych potrzebnych do rozwoju i optymalizacji inżynierii odwrotnej w kierunku drukowanie rozwiązań w pełni funkcjonalnych klinicznie

Object-based representation and analysis of light and electron microscopic volume data using Blender

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Rapid improvements in light and electron microscopy imaging techniques and the development of 3D anatomical atlases necessitate new approaches for the visualization and analysis of image data. Pixel-based representations of raw light microscopy data suffer from limitations in the number of channels that can be visualized simultaneously. Complex electron microscopic reconstructions from large tissue volumes are also challenging to visualize and analyze. RESULTS: Here we exploit the advanced visualization capabilities and flexibility of the open-source platform Blender to visualize and analyze anatomical atlases. We use light-microscopy-based gene expression atlases and electron microscopy connectome volume data from larval stages of the marine annelid Platynereis dumerilii. We build object-based larval gene expression atlases in Blender and develop tools for annotation and coexpression analysis. We also represent and analyze connectome data including neuronal reconstructions and underlying synaptic connectivity. CONCLUSIONS: We demonstrate the power and flexibility of Blender for visualizing and exploring complex anatomical atlases. The resources we have developed for Platynereis will facilitate data sharing and the standardization of anatomical atlases for this species. The flexibility of Blender, particularly its embedded Python application programming interface, means that our methods can be easily extended to other organisms.The research leading to these results received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/European Research Council Grant Agreement 260821

The Scalable Brain Atlas: instant web-based access to public brain atlases and related content

The Scalable Brain Atlas (SBA) is a collection of web services that provide unified access to a large collection of brain atlas templates for different species. Its main component is an atlas viewer that displays brain atlas data as a stack of slices in which stereotaxic coordinates and brain regions can be selected. These are subsequently used to launch web queries to resources that require coordinates or region names as input. It supports plugins which run inside the viewer and respond when a new slice, coordinate or region is selected. It contains 20 atlas templates in six species, and plugins to compute coordinate transformations, display anatomical connectivity and fiducial points, and retrieve properties, descriptions, definitions and 3d reconstructions of brain regions. The ambition of SBA is to provide a unified representation of all publicly available brain atlases directly in the web browser, while remaining a responsive and light weight resource that specializes in atlas comparisons, searches, coordinate transformations and interactive displays.Comment: Rolf K\"otter sadly passed away on June 9th, 2010. He co-initiated this project and played a crucial role in the design and quality assurance of the Scalable Brain Atla

Two stream hypothesis of visual processing for navigation in mouse

Vision research has traditionally been studied in stationary subjects observing stimuli, and rarely during navigation. Recent research using virtual reality environments for mice has revealed that responses even in the primary visual cortex are modulated by spatial context - identical scenes presented in different positions of a room can elicit different responses. Here, we review these results and discuss how information from visual areas can reach navigational areas of the brain. Based on the observation that mouse higher visual areas cover different parts of the visual field, we propose that spatial signals are processed along two-streams based on visual field coverage. Specifically, this hypothesis suggests that landmark related signals are processed by areas biased to the central field, and self-motion related signals are processed by areas biased to the peripheral field

Common Atlas Format and 3D Brain Atlas Reconstructor: Infrastructure for Constructing 3D Brain Atlases

One of the challenges of modern neuroscience is integrating voluminous data of diferent modalities derived from a variety of specimens. This task requires a common spatial framework that can be provided by brain atlases. The first atlases were limited to two-dimentional presentation of structural data. Recently, attempts at creating 3D atlases have been made to offer navigation within non-standard anatomical planes and improve capability of localization of different types of data within the brain volume. The 3D atlases available so far have been created using frameworks which make it difficult for other researchers to replicate the results. To facilitate reproducible research and data sharing in the field we propose an SVG-based Common Atlas Format (CAF) to store 2D atlas delineations or other compatible data and 3D Brain Atlas Reconstructor (3dBAR), software dedicated to automated reconstruction of three-dimensional brain structures from 2D atlas data. The basic functionality is provided by (1) a set of parsers which translate various atlases from a number of formats into the CAF, and (2) a module generating 3D models from CAF datasets. The whole reconstruction process is reproducible and can easily be configured, tracked and reviewed, which facilitates fixing errors. Manual corrections can be made when automatic reconstruction is not sufficient. The software was designed to simplify interoperability with other neuroinformatics tools by using open file formats. The content can easily be exchanged at any stage of data processing. The framework allows for the addition of new public or proprietary content

Collating and Curating Neuroanatomical Nomenclatures: Principles and Use of the Brain Architecture Knowledge Management System (BAMS)

Terms used to describe nervous system parts and their interconnections are rife with synonyms, partial correspondences, and even homonyms, making effective scientific communication unnecessarily difficult. To address this problem a new Topological Relations schema for the Relations module of BAMS (Brain Architecture Knowledge Management System) was created. It includes a representation of the qualitative spatial relations between nervous system parts defined in different neuroanatomical nomenclatures or atlases and is general enough to record data and metadata from the literature, regardless of description level or species. Based on this foundation a Projections Translations inference engine was developed for the BAMS interface that automatically translates neuroanatomical projection (axonal inputs and outputs) reports across nomenclatures from translated information. To make BAMS more useful to the neuroscience community three things were done. First, we implemented a simple schema for validation of the translated neuroanatomical projections. Second, more than 1,000 topological relations between brain gray matter regions for the rat were inserted, along with associated details. Finally, a case study was performed to enter all historical or legacy published information about terminology related to one relatively complex gray matter region of the rat. The bed nuclei of the stria terminalis (BST) were chosen and 21 different nomenclatures from 1923 to present were collated, along with 284 terms for parts (gray matter differentiations), 360 qualitative topological relations between parts, and more than 7,000 details about spatial relations between parts, all of which was annotated with appropriate metadata. This information was used to construct a graphical “knowledge map” of relations used in the literature to describe subdivisions of the rat BST

A Bottom-up Approach to Data Annotation in Neurophysiology

Metadata providing information about the stimulus, data acquisition, and experimental conditions are indispensable for the analysis and management of experimental data within a lab. However, only rarely are metadata available in a structured, comprehensive, and machine-readable form. This poses a severe problem for finding and retrieving data, both in the laboratory and on the various emerging public data bases. Here, we propose a simple format, the “open metaData Markup Language” (odML), for collecting and exchanging metadata in an automated, computer-based fashion. In odML arbitrary metadata information is stored as extended key–value pairs in a hierarchical structure. Central to odML is a clear separation of format and content, i.e., neither keys nor values are defined by the format. This makes odML flexible enough for storing all available metadata instantly without the necessity to submit new keys to an ontology or controlled terminology. Common standard keys can be defined in odML-terminologies for guaranteeing interoperability. We started to define such terminologies for neurophysiological data, but aim at a community driven extension and refinement of the proposed definitions. By customized terminologies that map to these standard terminologies, metadata can be named and organized as required or preferred without softening the standard. Together with the respective libraries provided for common programming languages, the odML format can be integrated into the laboratory workflow, facilitating automated collection of metadata information where it becomes available. The flexibility of odML also encourages a community driven collection and definition of terms used for annotating data in the neurosciences

Matching spatial with ontological brain regions using Java tools for visualization, database access, and integrated data analysis.

Contains fulltext : 75177reid.pdf (publisher's version ) (Closed access)Brain atlases are widely used in experimental neuroscience as tools for locating and targeting specific brain structures. Delineated structures in a given atlas, however, are often difficult to interpret and to interface with database systems that supply additional information using hierarchically organized vocabularies (ontologies). Here we discuss the concept of volume-to-ontology mapping in the context of macroscopical brain structures. We present Java tools with which we have implemented this concept for retrieval of mapping and connectivity data on the macaque brain from the CoCoMac database in connection with an electronic version of "The Rhesus Monkey Brain in Stereotaxic Coordinates" authored by George Paxinos and colleagues. The software, including our manually drawn monkey brain template, can be downloaded freely under the GNU General Public License. It adds value to the printed atlas and has a wider (neuro-)informatics application since it can read appropriately annotated data from delineated sections of other species and organs, and turn them into 3D registered stacks. The tools provide additional features, including visualization and analysis of connectivity data, volume and centre-of-mass estimates, and graphical manipulation of entire structures, which are potentially useful for a range of research and teaching applications