10 Essential Flywheel Gears to Supercharge Your Imaging Workflows

One of the most exciting features of the Flywheel medical imaging data management platform is the ability to leverage Gears, which are vetted, containerized algorithms that run within the system and automate routine imaging tasks. Whether you’re extracting metadata, running classification or quality assurance, or converting imaging formats, Gears make it possible to do so efficiently, with reproducible results.

Below, we explore 10 of our most-loved Gears and Gear bundles that showcase Flywheel’s versatility and impact across a wide range of use cases.

1. File Metadata and Classification Gears

Gears included: file-metadata-importer, file-classifier

These gears are designed to index file metadata and classify files based on their modality and type. They support a wide variety of file formats and are foundational to any data curation pipeline.

Why they matter: These gears are installed by default and often serve as the backbone for gear rule automation. They enable efficient search, reporting and organization of data, making them indispensable for managing large datasets.

2. DICOM Curation Gears

Gears included: dicom-fixer, dicom-splitter, dicom-qc, dicom-editor

This suite of gears handles the full spectrum of DICOM file curation, from fixing inconsistencies to splitting and editing files.

Why they matter: These gears are essential for standardizing DICOM data according to your project’s requirements. They can be chained together using gear rules to automate workflows, ensuring consistent and high-quality data curation.

3. CT Total Segmentator

Gear included: ct-total-segmentator

This gear segments whole-body CT scans using a state-of-the-art open-source algorithm.

Why it matters: Segmenting whole-body CT scans can lead to more accurate diagnoses, improved disease monitoring, and better quantitative analysis of organ structure and function. This gear also demonstrates the power of Flywheel to integrate popular open-source algorithms like this one and package it as a gear to make it available to use on the platform.

4. De-identification Gears

Gears included: deid-inplace, deid-export, pii-image-detector, pii-image-redactor

These gears perform de-identification tasks on both metadata and pixel data, supporting a wide range of file types and actions.

Why they matter: Ensuring data privacy is critical in healthcare and research. These gears help remove personally identifiable information (PII) and protected health information (PHI), enabling compliance with regulations like HIPAA and GDPR.

5. Ophthalmology Processing Gears

Gears included: oct-converter, oct-qa

This bundle supports the processing and quality assurance of ophthalmology imaging data, particularly OCT (Optical Coherence Tomography).

Why they matter: These gears highlight Flywheel’s capabilities beyond traditional radiology, supporting specialized domains such as ophthalmology that may not be supported by more generalized platforms without heavy customization.

6. Custom Script Runners

Gears included: file-curator, hierarch-curator

These gears allow users to run generic Python scripts on individual files or entire Flywheel projects.

Why they matter: They provide flexibility for users to automate custom tasks without needing to develop a dedicated gear for each one. This is ideal for teams that frequently run bespoke scripts as part of their workflow.

7. File Conversion Gears

Gears included: dcm2niix, wsi-to-dicom, dcm2mips, nifti-to-mips, roi2nix, oct-converter

This collection of gears enables conversion between various file formats, supporting interoperability across data types.

Why they matter: File conversion is often a necessary and time-consuming step in data curation and analysis. These gears streamline the process, allowing users to prepare data for downstream workflows with minimal effort.

8. Non-Medical Data Import Gears

Gears included: form-importer, csv-import, file-validator

These gears are designed to process non-imaging data such as electronic medical records (EMRs), in formats like CSV and JSON.

Why they matter: Flywheel primarily helps teams manage imaging data. But these gears demonstrate how the platform can be used to manage and validate diverse data types, making it a versatile tool for medical research.

9. PETSurfer Gears

Gears included: PETSurfer suite (petsurfer-coreg, petsurfer-mc, petsurfer-roi)

This suite is based on the PETSurfer framework and supports integrated MRI-PET analysis.

Why they matter: These gears showcase advanced post-processing capabilities and illustrate how Flywheel can automate complex multimodal imaging workflows, making high-level analysis more accessible and reproducible.

10. Quality Control and Validation Gears

Gears included: mriqc, oct-qa, dicom-qc, file-validator, session-validator

These gears perform quality control and validation across a range of modalities and applications.

Why they matter: Data quality is crucial for reliable, reproducible analysis. These gears help ensure that datasets meet required standards before they are used for resource-intensive research.

Let’s Build Your Gear Strategy

Flywheel’s gear ecosystem is designed to empower your data workflows, whether your focus is on curating and converting or validating and analyzing. These top gear bundles represent just a fraction of what’s possible with the 100+ gears currently available in the Flywheel Gear Exchange. If you’re ready to explore how these gears can support your team — or if you’re interested in developing custom gears tailored to your specific needs — we’re here to help.