MVA.ai Medical Volume Annotator

Customized,
On-Premise Annotation Platform

On-premise or cloud

Efficient annotation Web‑UI

Flexible collaborative workflows

Customized to your project

Joint research grant applications

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Use Cases

Annotation and labeling for the development of AI models as well as non‑AI research projects

Get feedback on model predictions from your medical experts throughout a project's lifetime

Collaboration between stakeholders – medical experts, AI researchers, annotators, reviewers, project leads ...

Data privacy requirements – MVAI.ai runs entirely on-site, no data leaves your institution! Alternatively, MVA.ai can run on any cloud server or be hosted for you

Research consortia – easily collaborate across multiple sites, distribute the annotation / review process and manage your research project

Annotation & Labeling

Efficient in-browser annotation accelerated by super-pixel based brushes and slice to slice propagation of annotations

Slice and volume labels for time-saving expert input

CT, MRI, X-Ray, 2D+time – MVA.ai supports whole-body CTs as well as multi-sequence MRIs

More annotation features

Workflows & Collaboration

Organize your project into annotation, review, correction ...

Assign different stakeholders to each node

Reviews using customizable UI forms per slice/volume

More about workflows

Roadmap & Custom Features

Eye-tracking – obtain 2D and 3D (!) heatmaps of what experts pay attention to while assessing medical data

Model Zoo – Segment and classify your image data using state of the art open-source AI models

Train your own AI models directly within MVA.ai

Active learning / Integration with Monai Label – annotate only the data that will actually improve your AI model

Additional modalities – work with µCT, 2D microscopy slides and large 3D microscopy data

Custom functionality

Contact MVA.ai if you need specific features for your research project!

Contact MVA