Control

Bringing people, systems, and workflows together

  • Connect care teams, systems, and workflows across technologies, sites, and treatment approaches
  • Support treatment continuity with the optional Backup Planner, enabling efficient plan conversion across machines, techniques, and energies
  • Maintain flexibility across current and next-generation treatment delivery systems, including Accela
  • Benefit from GPU-enabled optimization algorithms that help accelerate treatment plan generation
  • Tailor planning workflows with scripting and automation tools to support institutional needs

AI Contouring — Integrated in Eclipse

High-quality, deep learning-driven structure delineation directly within your treatment planning workflow

  • Automatically generates contours from CT and MRI images for over 200 anatomical structures — including organs-at-risk, lymph nodes, and brain metastases
  • Built on multiple clinical guidelines (RTOG, ESTRO, DAHANCA, and more) for consistent, evidence-based results
  • Seamlessly integrated within Eclipse, with no additional hardware, no separate platform, and single vendor support
  • Validated by leading institutions2,3 worldwide, with auto contouring results on par with expert physicians4

A propósito de Eclipse

Entrega de tratamientos

Explore the entire Digital Oncology portfolio

Le ayudaremos a conectarse

Nuestros especialistas pueden ayudarle a integrar Varian a su flujo de trabajo.

Si es cliente actual y necesita soporte técnico para un producto, visite nuestra página de Atención al cliente

†Con marcado CE. 510(k) pendiente. No disponible para la venta en EE. UU. ni en todos los mercados y no se ofrecen garantías en cuanto a la comercialización o a la disponibilidad de las funciones.

*AI-Rad Companion Organs RT es un producto de Siemens Healthineers y una marca registrada de Siemens Healthineers en algunos países.

  1. Putz, F. Is AI-powered tumor autocontouring ready for clinical use? Evaluation of Siemens Healthineers research prototype for the autosegmentation of brain metastases. Poster presented at DEGRO; Jun 2025, Dresden, Germany. Datasets: 101 patients; 391 lesions; 1.5T MRI series T1-SPACE. University of Erlangen-Nuremberg.
  2. Tezcanli, E. Evaluation of a Deep Learning Contouring Algorithm for Target Delineation in HyperArc Radiosurgery. Webinar, Oct 2025. Datasets: 10 patients; 82 lesions. Acibadem University.