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Medical imaging teams need annotation accuracy that directly impacts patient outcomes. Avala provides precision annotation tools, multi-stage quality control workflows, and team permission controls that support the rigorous standards clinical-grade training data requires.

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Why Avala for Medical Imaging

Medical annotation differs from general computer vision in its tolerance for error — there is essentially none. A missed lesion boundary or misclassified cell type can propagate through the model and affect diagnostic decisions. Avala addresses this with:

Precision Annotation Tools

Polygon and segmentation tools with sub-pixel precision for accurate boundary delineation. Keypoint tools for anatomical landmark placement.

Multi-Stage Quality Control

Configurable review pipelines with spot-checking, targeted review, and full review stages. Support for domain expert reviewers with role-based access.

Team Permissions

Fine-grained access controls to restrict who can view, annotate, and review sensitive medical data. Role-based permissions at the organization, team, and project level.

Audit and Compliance

Task lifecycle tracking from assignment through review and approval. Every annotation action is recorded for audit trail requirements.

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Data Types

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Common Tasks

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Lesion Detection

Draw bounding boxes or polygons around tumors, nodules, cysts, and other regions of interest. For tasks that require precise boundary delineation (e.g., tumor segmentation for surgical planning), use the polygon tool to trace exact margins. The polygon tool supports:
  • Freeform vertex placement for irregular shapes
  • Edge snapping for clean boundaries
  • Vertex editing to refine placement after initial tracing
  • Sub-pixel accuracy for high-resolution medical images

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Organ Segmentation

Create pixel-level segmentation masks for organs and anatomical structures in CT or MRI slices. Use the segmentation brush for large regions and switch to polygon mode for fine boundary work.
For organ segmentation tasks, define your label taxonomy with clear hierarchy: organ system > organ > substructure. For example: cardiovascular > heart > left_ventricle. This makes the annotation process faster and the resulting data more useful for model training.

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Cell Classification

Classify cell types in pathology slides using classification labels and structured attributes. Define a taxonomy that includes:
  • Primary cell type (e.g., lymphocyte, neutrophil, epithelial)
  • Morphological attributes (e.g., size, shape regularity, staining intensity)
  • Diagnostic relevance (e.g., normal, atypical, malignant)
Classification can be applied at the object level (individual cells) or scene level (tissue regions).

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Surgical Video Analysis

Track surgical instruments and anatomical landmarks across endoscopy or surgical video frames. Object tracking maintains consistent IDs across frames, making it possible to train models for instrument detection, phase recognition, and activity analysis.

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Quality Control for Medical Data

Medical annotation quality control goes beyond general-purpose review. Avala’s quality control features support the workflows medical teams require.

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Multi-Stage Review Pipelines

Configure review pipelines that match your clinical validation process:
At each stage, reviewers can approve, reject with comments, or flag specific annotations with issues. Rejected tasks return to the annotator with clear feedback.

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Annotation Issues

Pin issues to specific annotations in the image. A reviewer can mark a polygon boundary as “too loose at the superior margin” and the annotator sees the issue pinned to the exact location that needs correction.

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Consensus Workflows

For validation datasets and ground truth creation, assign the same images to multiple domain experts independently. Consensus scoring reveals:
  • Regions where experts disagree (these need additional review or clearer guidelines)
  • Annotators who consistently deviate from the group
  • Edge cases where the annotation guideline is ambiguous

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Quality Metrics

Monitor annotation quality across your team:

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Compliance Considerations

Medical imaging data often falls under regulatory requirements (HIPAA, GDPR, MDR). While Avala provides the tooling for annotation workflows, your team is responsible for ensuring data handling complies with applicable regulations. Avala features that support compliance workflows:
Avala does not provide HIPAA BAA or DICOM integration out of the box. If your workflow requires these, contact support@avala.ai to discuss your compliance requirements before uploading protected health information.

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Avala Features Used

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Getting Started

1

Set up your organization

Create an organization and invite your annotation and review teams. Configure team roles so that only authorized users can access medical data.
2

Upload imaging data

Create a dataset with the appropriate data type (Image for radiology/pathology, Video for endoscopy) and upload your files.
3

Define your label taxonomy

Set up object classes, attributes, and classification categories that match your clinical annotation guideline. Include clear definitions and reference examples.
4

Configure quality control

Set up a multi-stage review pipeline. Assign domain expert reviewers and configure acceptance criteria.
5

Annotate, review, and export

Annotators label the data, reviewers validate at each stage, and you export the approved annotations for model training.

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Next Steps

Quality Control

Set up multi-stage review workflows with spot-checking and expert review.

Polygon Tool

Precision boundary tracing for lesions, organs, and anatomical structures.

Team Permissions

Configure role-based access controls for your organization.

Label Taxonomy

Design an effective label schema for medical annotation projects.