Dataset Information

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

ABSTRACT: 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 calculate new metrics. This tool greatly facilitates the process of neuronal reconstruction, bridging the gap between existing proprietary tools to optimize neuroscientific workflows.

SUBMITTER: Velasco I

PROVIDER: S-EPMC7646287 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Velasco Ivan I Toharia Pablo P Benavides-Piccione Ruth R Fernaud-Espinosa Isabel I Brito Juan P JP Mata Susana S DeFelipe Javier J Pastor Luis L Bayona Sofia S

Frontiers in neuroanatomy 20201006

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 softw ...[more]

PMID: 33192345

Similar Datasets

Project description:BackgroundSepsis has a mortality rate of 40 %, which can be halved if the evidence-based "Sepsis Six" care bundle is implemented within 1 h. UK audit shows low implementation rates. Interventions to improve this have had minimal effects. Quality improvement programmes could be further developed by using theoretical frameworks (Theoretical Domains Framework (TDF)) to modify existing interventions by identifying influences on clinical behaviour and selecting appropriate content. The aim of this study was to illustrate using this process to modify an intervention designed using plan-do-study-act (P-D-S-A) cycles that had achieved partial success in improving Sepsis Six implementation in one hospital.MethodsFactors influencing implementation were investigated using the TDF to analyse interviews with 34 health professionals. The nursing team who developed and facilitated the intervention used the data to select modifications using the Behaviour Change Technique (BCT) Taxonomy (v1) and the APEASE criteria: affordability, practicability, effectiveness, acceptability, safety and equity.ResultsFive themes were identified as influencing implementation and guided intervention modification. These were:(1) "knowing what to do and why" (TDF domains knowledge, social/professional role and identity); (2) "risks and benefits" (beliefs about consequences), e.g. fear of harming patients through fluid overload acting as a barrier to implementation versus belief in the bundle's effectiveness acting as a lever to implementation; (3) "working together" (social influences, social/professional role and identity), e.g. team collaboration acting as a lever versus doctor/nurse conflict acting as a barrier; (4) "empowerment and support" (beliefs about capabilities, social/professional role and identity, behavioural regulation, social influences), e.g. involving staff in intervention development acting as a lever versus lack of confidence to challenge colleagues' decisions not to implement acting as a barrier; (5) "staffing levels" (environmental context and resources), e.g. shortages of doctors at night preventing implementation. The modified intervention included six new BCTs and consisted of two additional components (Sepsis Six training for the Hospital at Night Co-ordinator; a partnership agreement endorsing engagement of all clinical staff and permitting collegial challenge) and modifications to two existing components (staff education sessions; documents and materials).ConclusionsThis work demonstrates the feasibility of the TDF and BCT Taxonomy (v1) for developing an existing quality improvement intervention. The tools are compatible with the pragmatic P-D-S-A cycle approach generally used in quality improvement work.

Project description:We present pyOsiriX, a plugin built for the already popular dicom viewer OsiriX that provides users the ability to extend the functionality of OsiriX through simple Python scripts. This approach allows users to integrate the many cutting-edge scientific/image-processing libraries created for Python into a powerful DICOM visualisation package that is intuitive to use and already familiar to many clinical researchers. Using pyOsiriX we hope to bridge the apparent gap between basic imaging scientists and clinical practice in a research setting and thus accelerate the development of advanced clinical image processing. We provide arguments for the use of Python as a robust scripting language for incorporation into larger software solutions, outline the structure of pyOsiriX and how it may be used to extend the functionality of OsiriX, and we provide three case studies that exemplify its utility. For our first case study we use pyOsiriX to provide a tool for smooth histogram display of voxel values within a user-defined region of interest (ROI) in OsiriX. We used a kernel density estimation (KDE) method available in Python using the scikit-learn library, where the total number of lines of Python code required to generate this tool was 22. Our second example presents a scheme for segmentation of the skeleton from CT datasets. We have demonstrated that good segmentation can be achieved for two example CT studies by using a combination of Python libraries including scikit-learn, scikit-image, SimpleITK and matplotlib. Furthermore, this segmentation method was incorporated into an automatic analysis of quantitative PET-CT in a patient with bone metastases from primary prostate cancer. This enabled repeatable statistical evaluation of PET uptake values for each lesion, before and after treatment, providing estaimes maximum and median standardised uptake values (SUVmax and SUVmed respectively). Following treatment we observed a reduction in lesion volume, SUVmax and SUVmed for all lesions, in agreement with a reduction in concurrent measures of serum prostate-specific antigen (PSA).

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets