FlywheelTools: Data Curation and Manipulation on the Flywheel Platform

Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation

Objective

Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data.

Methods

We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates. The algorithm creates directories based on keywords in new file names.

Results

Our work focuses on organizing computed tomography (CT), magnetic resonance (MR) images, patient clinical data, and segmented annotations. In addition, an AI model is used to generate different initial labels that can be edited manually to create ground truth labels. The manually verified ground truth labels are later included in the structured dataset using an automated algorithm for future research.

Conclusion

This is a workflow with an AI model trained on local hospital medical data with output based/adapted to the users and their preferences. The automated algorithms and AI model could be implemented inside a secondary secure environment in the hospital to produce inferences.

aXonica: A support package for MRI based Neuroimaging

Magnetic Resonance Imaging (MRI) assists in studying the nervous system. MRI scans undergo significant processing before presenting the final images to medical practitioners. These processes are executed with ease due to excellent software pipelines. However, establishing software workstations is non-trivial and requires researchers in life sciences to be comfortable in downloading, installing, and scripting software that is non-user-friendly and may lack basic GUI. As researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines speeding up building software workstations and laboratories. Previous solutions include NeuroDebian, BIDS Apps, Flywheel, QMENTA, Boutiques, Brainlife and Neurodesk. Overall, all these solutions complement each other. NeuroDebian covers neuroscience and has a wider scope, providing only 51 tools for MRI. Whereas, BIDS Apps is committed to the BIDS format, covering only 45 software related to MRI. Boutiques is more flexible, facilitating its pipelines to be easily installed as separate containers, validated, published, and executed. Whereas, both Flywheel and Qmenta are propriety, leaving four for users looking for 'free for use' tools, i.e., NeuroDebian, Brainlife, Neurodesk, and BIDS Apps. This paper presents an extensive survey of 317 tools published in MRI-based neuroimaging in the last ten years, along with 'aXonica,' an MRI-based neuroimaging support package that is unbiased towards any formatting standards and provides 130 applications, more than that of NeuroDebian (51), BIDS App (45), Flywheel (70), and Neurodesk (85). Using a technology stack that employs GUI as the front-end and shell scripted back-end, aXonica provides (i) 130 tools that span the entire MRI-based neuroimaging analysis, and allow the user to (ii) select the software of their choice, (iii) automatically resolve individual dependencies and (iv) installs them. Hence, aXonica can serve as an important resource for researchers and teachers working in the field of MRI-based Neuroimaging to (a) develop software workstations, and/or (b) install newer tools in their existing workstations.

Tractometry of Human Visual White Matter Pathways in Health and Disease

Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.

Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.

ezBIDS: Guided standardization of neuroimaging data interoperable with major data archives and platforms

Data standardization promotes a common framework through which researchers can utilize others' data and is one of the leading methods neuroimaging researchers use to share and replicate findings. As of today, standardizing datasets requires technical expertise such as coding and knowledge of file formats. We present ezBIDS, a tool for converting neuroimaging data and associated metadata to the Brain Imaging Data Structure (BIDS) standard. ezBIDS contains four major features: (1) No installation or programming requirements. (2) Handling of both imaging and task events data and metadata. (3) Semi-automated inference and guidance for adherence to BIDS. (4) Multiple data management options: download BIDS data to local system, or transfer to OpenNeuro.org or to brainlife.io. In sum, ezBIDS requires neither coding proficiency nor knowledge of BIDS, and is the first BIDS tool to offer guided standardization, support for task events conversion, and interoperability with OpenNeuro.org and brainlife.io.

Continuous Automated Analysis Workflow for MRS Studies

Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review.The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps.The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory.Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages.

QuNex-An integrative platform for reproducible neuroimaging analytics

Introduction

Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability.

Methods

To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features.

Results

The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform.

Discussion

Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.

Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging

Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.

Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT)

Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due to proprietary issues and continuous technology development, preclinical images, unlike DICOM-based images, are often stored in an unstructured data file in company-specific proprietary formats. This limits the available DICOM-based image management database to be effectively used for preclinical applications. A centralized image registry and management tool is essential for advances in preclinical imaging research. Specifically, such tools may have a high impact in generating large image datasets for the evolving artificial intelligence applications and performing retrospective analyses of previously acquired images. In this study, a web-based server application is developed to address some of these issues. The application is designed to reflect the actual experimentation workflow maintaining detailed records of both individual images and experimental data relevant to specific studies and/or projects. The application also includes a web-based 3D/4D image viewer to easily and quickly view and evaluate images. This paper briefly describes the initial implementation of the web-based application.