Top 10 Best Surgery Planning Software of 2026

Arterys supports a data model that treats imaging and derived planning artifacts as linked entities, so measures and annotations stay attached to the underlying study and can be versioned through review cycles. The collaboration layer supports case sharing across teams, which reduces handoffs between imaging, clinical, and surgical stakeholders. Integration is oriented around provisioning access and connecting external systems via API for automation and throughput during high case volume.

A tradeoff appears when deployments require strict internal schema control because Arterys planning artifacts need mapping into an organization’s data and governance model. Arterys fits when surgical planning requires repeatable review workflows with documented API and automation hooks for integration testing and downstream system updates.

Merge fits teams that need surgery planning data to move across imaging, scheduling, and device or planning systems without manual re-entry. Its data model treats planning artifacts as structured records, not only files, which supports repeatable mappings between schemas. Automation can trigger on events like plan creation or case updates and then enforce configuration changes across connected systems.

A tradeoff appears in governance and implementation effort since schema design and integration mapping require upfront work before high throughput is reached. Merge fits best when multiple departments share the same planning entities and need consistent RBAC and audit trails, such as pre-op planning updates that propagate to OR scheduling and documentation systems.

Materialise Mimics supports end-to-end planning steps including DICOM import, segmentation, landmarking, and quantitative measurements tied to image space. Outputs include 3D surface and volumetric artifacts that can be exported for guide design and other downstream operations. Automation is possible through scripting and integration points inside the Materialise ecosystem, but the automation surface is less about headless API-first orchestration than about workflow repeatability. Extensibility is strongest when planning steps remain within the Materialise data and project conventions.

A practical tradeoff appears when governance and admin controls must be enforced across distributed teams that need consistent provisioning and schema validation. RBAC exists for account and project access, but enforcing fine-grained permissions per model object and audit log retention requires careful process design. Mimics fits situations where clinical teams need repeatable segmentation and measurement outputs and where the broader workflow pipeline already uses Materialise tools.

3D Slicer combines medical image visualization and surgical planning in a scriptable, extensible desktop environment. Its data model centers on volume, segmentation, models, and transforms, which supports consistent edits across modalities and registration steps.

Planning workflows are reproducible through saved scenes and Slicer modules, with automation available through Python scripting and the built-in command-line interface. Integration depth is driven by extensibility via scripted modules and import/export hooks that connect imaging and segmentation pipelines.

OsiriX performs offline DICOM visualization and surgical planning workflows using image and annotation data stored in patient studies. It supports measurement, segmentation workflows, and export of annotated outputs for case documentation and intra-team sharing.

Integration depth is driven by DICOM-native data handling and file-based exchange rather than a centralized server automation layer. Extensibility relies more on workflow configuration and plugin-like customization than on a documented enterprise API or governed automation surface.

Horos targets surgery planning teams that need structured case data, repeatable imaging-driven workflows, and audit-friendly collaboration. Its data model centers on patient, imaging, and plan artifacts with a configuration approach that supports consistent study templates.

Integration depth is built around extensibility and interoperability patterns used by imaging ecosystems, with an automation surface that supports scripted processing and reproducible planning steps. Admin and governance controls focus on controlling access at the user and project level while retaining traceable edits across planning artifacts.

Stryker 3D Planning ties surgical planning workflows to Stryker’s imaging and device ecosystem, which matters for integration depth. The core capabilities center on 3D visualization, anatomical modeling, and plan-to-procedure communication using structured planning outputs.

Governance depends on how administrators configure roles, project access, and case data retention within the deployment environment. Automation and extensibility hinge on the availability and completeness of Stryker’s integration and API surface for moving plans, measurements, and annotations between systems.

Brainlab is surgery planning software with deep imaging, navigation, and workflow integration built around clinical document and device data. Planning workspaces connect imaging, segmentation, measurements, and surgical guidance inputs into a single controlled workflow.

Integration depth centers on interoperability with hospital systems and imaging modalities plus downstream use in intraoperative navigation. Extensibility is expressed through documented integrations and automation surfaces that support configuration, governance, and data movement between systems.

Medtronic StealthStation supports image-guided surgery planning and intraoperative navigation with multimodal imaging and registration workflows. It focuses on an explicit data model for patient imaging, coordinate transforms, and surgical planning outputs that must stay consistent through the navigation lifecycle.

Integration depth is driven by device connectivity and workflow hooks that feed navigation states into operative guidance screens. Automation depends on how deployments can configure workflow, roles, and data handoffs, with an API surface that determines extensibility and integration throughput.

Sectra OrthoVision is a surgery planning software used for orthopedic workflows that center on structured patient data and image-driven planning. It integrates into clinical and imaging environments through configurable interfaces, including worklist and viewer interoperability for routine planning throughput.

The data model supports plan artifacts such as measurements, segmentations, and surgical plans that can be reused across sessions with consistent governance. Automation and extensibility are primarily realized through integration hooks and controlled access patterns that support admin configuration, RBAC, and auditability.