Radiotherapy teams are managing growing clinical demands, increasing case complexity, and greater expectations for consistency, often without additional time or resources. Contouring remains essential, but it can also be one of the most time-intensive and variable stages of treatment planning.
AutoContour v2.8 builds on the unified platform introduced in v2.7, which brought AutoContour and Limbus Contour technology together in a single solution. The integration was developed with the active involvement of the Limbus founders and team, many of whom continue to contribute to AutoContour and its Zero-Click workflows.
Now with 520+ AI-trained and guideline-aligned models across CT, MR, and CBCT, AutoContour v2.8 broadens anatomical coverage and adds automation for contour generation, post-processing, and workflow management. The goal is a more efficient path from image to plan-ready structures, with clinical review and decision-making remaining firmly in the care team's hands.
The latest version of AutoContour expands coverage across several high-value clinical areas, including:
Mediastinal lymph node models for more detailed thoracic contouring workflows
EPTN brain MR structures, including the caudate nucleus, fornix, periventricular space, and pineal gland
New and updated models across the head and neck, chest, abdomen, and pelvis
New MR abdomen models, extending AutoContour’s multimodality coverage
Among the latest additions are detailed mediastinal lymph-node models, further expanding support for thoracic contouring workflows.
The library also includes guideline-specific alternatives for areas where contouring approaches vary. Drawing on recognized international references where applicable, these models help departments build more consistent structure sets while selecting the conventions that best match their clinical protocols.
Generating anatomical contours is only the beginning. Before optimization, dosimetrists and planners often need to create margins, rings, crops, overlaps, and other planning structures.
AutoContour v2.8 introduces Clean and Smooth functions within Planning Structures to automate common post-processing tasks. Used with existing Boolean and structure-generation tools, they reduce repetitive editing and support a more standardized approach to structure preparation.
These rules can be configured in templates and automatically applied via Zero-Click, enabling anatomical contours and associated planning structures to be generated without additional manual steps. This helps departments apply their preferred approach more consistently across planners, disease sites, and locations, creating a more complete pathway toward plan-ready structures.
Zero-Click can match incoming images to predefined workflows and process them without manually launching each case. AutoContour v2.8 gives teams greater visibility and control over that automated activity.
The dashboard now makes it easier to see what is waiting, processing, or needs attention. Teams can move an urgent case to the top of the queue and rerun cases with a different template. Combined with parallel processing, these enhancements help busy, multisite departments manage higher volumes while maintaining oversight.
AutoContour v2.8 offers cloud and on-premises processing, allowing organizations to choose the approach that best fits their infrastructure, security policies, and regional requirements. On-premises environments can use parallel CPU processing with optional GPU support, while cloud deployment provides scalable model processing without relying on local compute resources.
Integration with Eclipse through ESAPI and support for vendor-neutral DICOM workflows allow departments to introduce automated contouring within established clinical environments, rather than being limited to a single treatment-planning ecosystem.
AutoContour brings contouring, planning-structure generation, rigid and deformable registration, and dose deformation into a single platform, supporting workflows beyond initial structure delineation, including anatomical change review and reirradiation.
When used with ClearCheck, registered images and deformed-dose information can support the review of cumulative physical and biological dose, including BED and EQD2. The software provides structured, reproducible information, while registrations, contours, dose mapping, and treatment decisions remain subject to qualified clinical review.
AI-generated contours support, rather than replace, clinical expertise. Performance can vary with image quality, acquisition protocols, anatomy, pathology, surgical changes, implants, and other factors that differ from the training data. Every structure should be reviewed in accordance with departmental policy before clinical use.
By automating repetitive steps and promoting consistency, AutoContour v2.8 gives teams a stronger starting point and more time for the work that requires clinical judgment most.