NVIDIA Healthcare Germany

Siemens Healthineers

Siemens Healthineers needed to speed up radiation-oncology model development and reduce the time clinicians spend contouring organs at risk so treatment planning could scale with rising cancer demand.

100 Gbps Mellanox InfiniBand network
24 PetaFlops of processing performance
3 PetaBytes of NVMe storage
47 organs

Reported impact

Business outcome

Siemens Healthineers automated organ contouring in radiation therapy, helped clinicians focus on other patient-care tasks, and built a training platform that supports hundreds of daily experiments and more than 45 approved AI applications.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Siemens Healthineers uses its Sherlock AI supercomputer to industrialize machine-learning development for radiation oncology, especially the organ-contouring step that slows treatment planning. Sherlock combines NVIDIA HGX-1 and HGX-2 servers loaded with NVIDIA Tesla V100 GPUs, NVLink interconnects, 3 petabytes of NVMe storage, and a 100 Gbps Mellanox InfiniBand fabric so researchers can keep GPUs saturated and run more than 600 deep-learning experiments each day. The cluster trains AI models behind syngo.via RT Image Suite, including AutoContouring models built on more than 4.5 million images. Once trained, those models automatically outline 47 organs for radiation-oncology workflows, giving clinicians faster starting points for therapy planning while the same platform supports more than 45 approved AI applications across the broader imaging portfolio.

Data flow

Curated medical images and related clinical data are used to train AutoContouring models on the Sherlock cluster, and the trained models are delivered through syngo.via RT Image Suite to automatically outline organs during radiation-therapy planning.

Solution architecture

6 components · 2 layers
  1. Compute
    • NVIDIA HGX-1 Provides part of the Sherlock supercomputer used for model training and experimentation.
    • NVIDIA HGX-2 Provides part of the Sherlock supercomputer used for model training and experimentation.
    • NVIDIA Tesla V100 Tensor Core GPUs Accelerate deep-learning training for contouring and broader medical-imaging models.
    • NVIDIA NVLink Supports high-bandwidth communication inside the GPU training servers.
  2. Serving
    • syngo.via RT Image Suite Delivers the AI-assisted AutoContouring workflow radiation oncologists use in treatment planning.
    • Mellanox InfiniBand Connects the Sherlock cluster with high-speed low-latency networking.

Architecture clues

  • AutoContouring models were trained on more than 4.5 million images
  • Sherlock uses NVMe storage and high-speed switching to keep GPU servers highly utilized
  • The broader AI program learns from a data lake of over 900 million curated images plus radiology, clinical, and genomic data

Evidence from the source

AI-assisted AutoContouring helps save time and improve standardization in organs-at-risk contouring.
So far, Sherlock has led to the development of more than 45 AI-powered applications approved for clinical use.
The system provides 24 PetaFlops of processing performance and 3 PetaBytes of NVMe storage, connected over a high-speed 100 Gbps Mellanox InfiniBand network, and is used to run over 600 deep learning experiments daily.
Trained on over 4.5 million images using the Sherlock supercomputer, the AI tool automatically outlines 47 organs.
Transparency Evidence & methodology 66/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value66/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. NVIDIA

    Siemens Healthineers customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/streamlining-cancer-radiation-therapy-with-ai/
    Source type
    Vendor-published customer story
    Published
    Aug 29, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Aug 29, 2025 · NVIDIA · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.