4,555 research outputs found
MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI
Many medical imaging techniques utilize fitting approaches for quantitative
parameter estimation and analysis. Common examples are pharmacokinetic modeling
in DCE MRI/CT, ADC calculations and IVIM modeling in diffusion-weighted MRI and
Z-spectra analysis in chemical exchange saturation transfer MRI. Most available
software tools are limited to a special purpose and do not allow for own
developments and extensions. Furthermore, they are mostly designed as
stand-alone solutions using external frameworks and thus cannot be easily
incorporated natively in the analysis workflow. We present a framework for
medical image fitting tasks that is included in MITK, following a rigorous
open-source, well-integrated and operating system independent policy. Software
engineering-wise, the local models, the fitting infrastructure and the results
representation are abstracted and thus can be easily adapted to any model
fitting task on image data, independent of image modality or model. Several
ready-to-use libraries for model fitting and use-cases, including fit
evaluation and visualization, were implemented. Their embedding into MITK
allows for easy data loading, pre- and post-processing and thus a natural
inclusion of model fitting into an overarching workflow. As an example, we
present a comprehensive set of plug-ins for the analysis of DCE MRI data, which
we validated on existing and novel digital phantoms, yielding competitive
deviations between fit and ground truth. Providing a very flexible environment,
our software mainly addresses developers of medical imaging software that
includes model fitting algorithms and tools. Additionally, the framework is of
high interest to users in the domain of perfusion MRI, as it offers
feature-rich, freely available, validated tools to perform pharmacokinetic
analysis on DCE MRI data, with both interactive and automatized batch
processing workflows.Comment: 31 pages, 11 figures URL: http://mitk.org/wiki/MITK-ModelFi
Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study
Developing robot agnostic software frameworks involves synthesizing the
disparate fields of robotic theory and software engineering while
simultaneously accounting for a large variability in hardware designs and
control paradigms. As the capabilities of robotic software frameworks increase,
the setup difficulty and learning curve for new users also increase. If the
entry barriers for configuring and using the software on robots is too high,
even the most powerful of frameworks are useless. A growing need exists in
robotic software engineering to aid users in getting started with, and
customizing, the software framework as necessary for particular robotic
applications. In this paper a case study is presented for the best practices
found for lowering the barrier of entry in the MoveIt! framework, an
open-source tool for mobile manipulation in ROS, that allows users to 1)
quickly get basic motion planning functionality with minimal initial setup, 2)
automate its configuration and optimization, and 3) easily customize its
components. A graphical interface that assists the user in configuring MoveIt!
is the cornerstone of our approach, coupled with the use of an existing
standardized robot model for input, automatically generated robot-specific
configuration files, and a plugin-based architecture for extensibility. These
best practices are summarized into a set of barrier to entry design principles
applicable to other robotic software. The approaches for lowering the entry
barrier are evaluated by usage statistics, a user survey, and compared against
our design objectives for their effectiveness to users
mockrobiota: a Public Resource for Microbiome Bioinformatics Benchmarking.
Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at http://caporaso-lab.github.io/mockrobiota/. The materials contained in mockrobiota include data set and sample metadata, expected composition data (taxonomy or gene annotations or reference sequences for mock community members), and links to raw data (e.g., raw sequence data) for each mock community data set. mockrobiota does not supply physical sample materials directly, but the data set metadata included for each mock community indicate whether physical sample materials are available. At the time of this writing, mockrobiota contains 11 mock community data sets with known species compositions, including bacterial, archaeal, and eukaryotic mock communities, analyzed by high-throughput marker gene sequencing. IMPORTANCE The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community
Land surface Verification Toolkit (LVT)
LVT is a framework developed to provide an automated, consolidated environment for systematic land surface model evaluation Includes support for a range of in-situ, remote-sensing and other model and reanalysis products. Supports the analysis of outputs from various LIS subsystems, including LIS-DA, LIS-OPT, LIS-UE. Note: The Land Information System Verification Toolkit (LVT) is a NASA software tool designed to enable the evaluation, analysis and comparison of outputs generated by the Land Information System (LIS). The LVT software is released under the terms and conditions of the NASA Open Source Agreement (NOSA) Version 1.1 or later. Land Information System Verification Toolkit (LVT) NOSA
SpaceTx: A Roadmap for Benchmarking Spatial Transcriptomics Exploration of the Brain
Mapping spatial distributions of transcriptomic cell types is essential to
understanding the brain, with its exceptional cellular heterogeneity and the
functional significance of its spatial organization. Spatial transcriptomics
techniques are hoped to accomplish these measurements, but each method uses
different experimental and computational protocols, with different trade-offs
and optimizations. In 2017, the SpaceTx Consortium was formed to compare these
methods and determine their suitability for large-scale spatial transcriptomic
atlases. SpaceTx work included progress in tissue processing, taxonomy
development, gene selection, image processing and data standardization, cell
segmentation, cell type assignments, and visualization. Although the landscape
of experimental methods has changed dramatically since the beginning of
SpaceTx, the need for quantitative and detailed benchmarking of spatial
transcriptomics methods in the brain is still unmet. Here, we summarize the
work of SpaceTx and highlight outstanding challenges as spatial transcriptomics
grows into a mature field. We also discuss how our progress provides a roadmap
for benchmarking spatial transcriptomics methods in the future. Data and
analyses from this consortium, along with code and methods are publicly
available at https://spacetx.github.io/
1st INCF Workshop on Sustainability of Neuroscience Databases
The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability
uTHCD: A New Benchmarking for Tamil Handwritten OCR
Handwritten character recognition is a challenging research in the field of
document image analysis over many decades due to numerous reasons such as large
writing styles variation, inherent noise in data, expansive applications it
offers, non-availability of benchmark databases etc. There has been
considerable work reported in literature about creation of the database for
several Indic scripts but the Tamil script is still in its infancy as it has
been reported only in one database [5]. In this paper, we present the work done
in the creation of an exhaustive and large unconstrained Tamil Handwritten
Character Database (uTHCD). Database consists of around 91000 samples with
nearly 600 samples in each of 156 classes. The database is a unified collection
of both online and offline samples. Offline samples were collected by asking
volunteers to write samples on a form inside a specified grid. For online
samples, we made the volunteers write in a similar grid using a digital writing
pad. The samples collected encompass a vast variety of writing styles, inherent
distortions arising from offline scanning process viz stroke discontinuity,
variable thickness of stroke, distortion etc. Algorithms which are resilient to
such data can be practically deployed for real time applications. The samples
were generated from around 650 native Tamil volunteers including school going
kids, homemakers, university students and faculty. The isolated character
database will be made publicly available as raw images and Hierarchical Data
File (HDF) compressed file. With this database, we expect to set a new
benchmark in Tamil handwritten character recognition and serve as a launchpad
for many avenues in document image analysis domain. Paper also presents an
ideal experimental set-up using the database on convolutional neural networks
(CNN) with a baseline accuracy of 88% on test data.Comment: 30 pages, 18 figures, in IEEE Acces
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