After conducting an extensive research of annotation tools, we chose V7 as it fits our needs best due to its customizable workflows and automated QA.Read case study ->
We use V7 to make our annotation and model training workflows more efficient. It's much easier to use than other software thanks to its intuitive UI.Read case study ->
We did a comparison when we started and noticed an increase in efficiency when we changed the workflow. Thanks to V7, we no longer have to waste time.Read case study ->
From X-rays to OCT scans, ultrasound videos, and volumetric DICOM series - import any type of medical images without compatibility issues. Visualize organs and annotations in cinematic 3D for easier navigation.
Assign user roles, delegate annotation tasks, and incorporate reviews to streamline your workflow. Set up blind tests to measure inter-reader variability between different human radiologists and AI models with Consensus Stages.
Connect AI models to your medical image annotation workflows to segment organs, detect anomalies, or identify technical flaws in your DCM files. Integrate external models like TotalSegmentator to label anatomical structures in CT images with AI, or use the platform to train your own custom medical AI.
Experience the capabilities of a state-of-the-art DICOM viewer, now enhanced and empowered by cutting-edge AI technology.
Use advanced filtering options to find specific DICOM image annotations, export data versions, and manage segmentation tasks by their status. Store your data in the cloud or establish custom storage environments to comply with legal regulations in your country.
Engage with your team in real-time, complete annotations together, and ensure consistent labeling across all DICOM files. Streamline communication and reduce errors with comments, notifications, and other collaborative features.
Connect V7 Darwin to any system or tech stack using the Darwin-py library. Migrate datasets, import annotations, access advanced reporting features, and complete DICOM preprocessing with easy-to-implement scripts and templates.
Hire professional annotators with a background in radiology. Enhance the accuracy of your medical imaging segmentation models by integrating reinforcement learning from human feedback. As you annotate and review DICOM files, V7 learns and adapts, providing improved AI suggestions over time.
Choose between cloud-based or on-premises configurations: Whether you prefer the flexibility of cloud-based solutions or the added security of on-premises setups, V7 offers both options.
The V7 platform for medical imaging annotation is compatible with Python. You can use V7's Darwin-py SDK to interact with the platform via the command line interface (CLI) or use it as a Python library. You can find the full documentation for Darwin-py here.
The pricing for medical image labeling services in V7 can vary depending on factors such as the complexity of the task, the volume of images to be labeled, and the level of accuracy and expertise required. Fill out the Get a Quote form on our website for a more accurate estimate based on your specific needs.
There are no special requirements beyond a reasonably modern computer and a stable internet connection. You will need Windows 10, or MacOS Monterrey or above. Also, to avoid performance drops, at least 8GB RAM is needed. V7 supports DICOM natively in 16-bit, which allows you to view images at their original quality. Additionally, V7 offers windowing features that enable you to see beyond what your monitor can typically display.
When exporting annotations, it is recommended to use Darwin JSON 2.0 format or NIfTI format. In Darwin JSON 2.0, annotations from each plane are saved in separate slots, and in NIfTI, exported annotations can be viewed in external 3D NIfTI viewers.
One of the most important parts of successful medical imaging annotation projects is incorporating review and consensus stages in your workflow to validate your annotations. Also, when working with volumetric data, you can leverage orthogonal views for accurate 3D annotation, and use interpolation to create in-between labels, speeding up the process. Lastly, maintaining a well-organized data structure, with separate tags or folders for each modality, body part, and disease, is crucial for an efficient labeling and training process. To find out more, read this guide to data labeling for radiology.
V7 offers a proprietary auto-annotate model that can automatically segment shapes within a selected area of a DCM file. These shapes can also be interpolated across different slices of a DICOM sequence. You can also use the SAM (Segment Anything Model) enhanced Auto-Annotate feature, which has been improved for accuracy, or contact us to develop a customized and fine-tuned segmentation model for your specific use case.
Before uploading a DICOM series to V7, it is recommended to zip the series together outside of V7 and rename the compressed file extension from .zip to .dcm. Once imported to V7, the individual DICOM slices will appear in a series. You can find out more in this guide about annotating DCM files in V7.
