A Complete Toolkit for DICOM
Annotation and Medical Image Analysis

Label regions of interest 10x faster with the best AI-copilot for medical imaging. Manage your DICOM datasets, create annotation workflows, and train your own medical ML models.

Build AI solutions for healthcare just like
Maleeha Nawaz
Maleeha Nawaz
Manager of Quality and Data Curation
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.
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Raman Muthuswamy
Raman Muthuswamy
Director, Translational Research at Genmab
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.
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Ryan Watson
Ryan Watson
Segmentation Manager at Intelligent Ultrasound
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.
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One NIfTI and DICOM Labeling Tool for All Modalities

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.

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AI Medical Image Annotation Tool with Top DICOM Viewer Features

Use MPR views, create multi-slot grids, and apply custom windowing or color maps for easier navigation. Try one-click DICOM segmentation with AI models fine-tuned for radiology and healthcare.


Use QA Workflows to Improve DICOM Annotation Accuracy 

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.


Speed Up Your DICOM Labeling Projects with Custom Models

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.

Annotate DICOM Files with Clinical Precision

Experience the capabilities of a state-of-the-art DICOM viewer, now enhanced and empowered by cutting-edge AI technology.

MPR Views. View and annotate DICOM images in multiple planes (axial, coronal, sagittal) simultaneously.
Window Levels. Adjust the brightness and contrast to enhance the visibility of specific structures.
3D Voxels. Dive deep into volumetric data with voxels and label DICOM medical images in three dimensions.
Pixel Masks. Create detailed segmentation masks at the pixel level for precise tumor or organ delineation.
Crosshairs. Pinpoint exact locations within volumetric series for accurate annotations and measurements.
Color Maps. Use color presets for different modalities to distinguish tissues and structures with ease.
Consensus. Add blind parallel reads by multiple labelers to ensure you have consistency and agreement across experts.
16-bit DICOM Support. Annotate and analyze your medical images with the highest level of detail.
Hanging Protocols. Set up predefined layouts, presets for display settings, and custom annotation instructions to ensure consistency.

How Intelligent Ultrasound used V7 to Double the Speed of their Training Data Pipelines

Labeling speed gain
Custom workflows
Favourite Feature
Custom workflows

Professional DICOM Labeling Services

Hire expert labelers to annotate DICOM file datasets of any size at competitive prices. Our team of trained medical annotators understands the nuances.
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Medical Talent
Seeking radiologists or other medical imaging professionals? Our network of expert labelers is skilled in annotating and interpreting medical images
V7 Software Training
AI errors often arise due to a labeler's unfamiliarity with the tools. Our experts know every hotkey, every feature, and every button.
Project Manager
DICOM labeling services come with a V7 project manager who designs workflows with you and ensures quality standards are met.
Security for Medical Data
Our platform maintains strict security protocols, including SOC2 and ISO27001 compliance, ensuring that labelers access only the DICOM images they are assigned to.

Experience the Leading Medical Annotation Workflow

Browse, Sort, and Filter Your DICOM Datasets in One Place

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.

Real-time Collaboration on DICOM Annotations

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.

Seamless Integration with PACS or DICOM SDK

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.

Leverage Humans in the Loop for AI DICOM Segmentation

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.


HIPAA/FDA-Ready and Secure - DICOM Data Safety Is Our Priority

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. 

GDPR Ready
ISO 27001
SOC2 Type 2
FDA Part 11
DICOM Standards
#1 DICOM Annotation Software for Medical AI
Solve any healthcare labeling task 10x faster and build generative AI for healthcare.
Asked Questions
Reach out to our support team or contact us for further questions
Which programming languages are compatible with the V7 platform?

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.

What is the pricing for V7's DICOM annotation services?

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.

Do I need any special hardware to use V7 for DICOM annotation?

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.

Which formats are used for DICOM annotations in V7?

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.

What are the best practices for annotating DCM files?

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.

Which AI models are available for DICOM annotation in V7?

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.

How does V7 handle volumetric DICOM series?

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