CT scans became radiology AI training data.

CT scan imagery was annotated for tumour segmentation so a radiology AI team could train and validate models with clearer medical imaging labels.

The challenge

Radiology AI needs precise tumour boundaries and consistent labels across CT scan volumes.

The client needed annotation support that could respect medical-imaging complexity and quality expectations.

The approach

SBL structured the annotation workflow around tumour segmentation rules, review gates and dataset preparation.

Quality checks focused on consistency and usability for downstream model training.

The result

The radiology AI team received cleaner labeled imaging data for model development and validation.

• CT scan regions were segmented for AI training.
• Review routines improved consistency across image batches.
• The dataset became easier to use in model workflows.
• The client avoided scaling all annotation capacity internally.