Deep learning-based medical image segmentation support for radiotherapy contouring workflows.
Uses trained medical imaging models to identify anatomical structures and generate contour masks from CT image volumes.
The workflow is planned around CT DICOM input and radiotherapy planning requirements.
Users can select required structures, allowing site-specific and organ-specific contour generation.
Outputs are intended for clinical review and correction, supporting quality assurance before final approval.
Manual contouring is time-consuming and may vary between users. AI-assisted segmentation can create a strong starting point, helping clinicians focus more on verification, refinement, and treatment planning decisions.