Made in Vienna
On-Premise Annotation Platform
On-premise or cloud
Efficient annotation WebUI
Flexible collaborative workflows
Customized to your project
Joint research grant applications
Use Cases
Annotation and labeling for the development of AI models as well as nonAI 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 – runs entirely on-site, no data leaves your institution! Alternatively, 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 – supports whole-body CTs as well as multi-sequence MRIs
Workflows & Collaboration
Organize your project into annotation, review, correction ...
Assign different stakeholders to each node
Reviews using customizable UI forms per slice/volume
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
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 if you need specific features for your research project!
Thanks to everyone who participated in the MICCAI 2023 raffle – the winner has been notified!
Made in Vienna