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Artificial Intelligence Services

Image segmentation, contouring, object detection, classification and regression tasks are now available with unseen accuracy and computational speed in medical workflows. We offer this technology in a customer-centered workflow and help you to reduce the time to market. We provide customer-based services that combine all stages necessary to incorporate deep learning into your applications. This comprises data annotation, network design, network training on dedicated hardware, adaptations of model design, and customized software development to achieve outstanding results.

Our vision is to bring AI solutions and our expertise into your business.


Data Annotation

  • In-house efficient annotation tools for 2D, 3D and 4D data 
  • Annotation by our medical experts
  • Defined quality standards for annotations
  • Planning and support with data acquisition
  • Build customized anatomical data bases

Additional benefits

  • Experience from successful past projects
  • Receive annotations in standard formats
  • Keep ownership of data and annotations

Model Design and Training

  • Custom neural network design

  • Problem-specific pre-processing

  • Dedicated high performance hardware

Additional Benefits

  • Learn from our experienced AI experts

  • Reduce engineering time

  • Fast evolving technology

Customer Integration

  • Delivery of trained model and annotated data
  • Software SDK and API (C / C++)
  • Source code of model design
  • API for hassle-free integration

Additional Benefits

  • Certified expert medical software development
  • Model reusable for future applications
  • Platform-independent software
  • CPU and GPU supported

Lifetime Management and Support

  • Refinement and adaptions during clinical use-test
  • Re-training with additional data
  • Keep model design and software up to date

Additional Benefits

  • State-of-the-art technology
  • Experience in regulatory requirements

Possible applications where Deep Learning could be applied to:


  • 2D radiographic images
  • 3D MR, CT, PET, and SPECT

  • Bones (spine,...)
  • Orthopaedics: Implant planning
  • Dental: Implant Planning (nerve canal, bones, teeth,...)
  • Dental: Caries detection
  • Organs (liver, kidney,...): volumetry
  • Dosimetry
  • Brain
  • Cardiac: ejection fraction
  • 3D printing


  • Recognition of MR series (Hanging Protocols)
  • Recognition of body parts (Hanging Protocols)

Object Detection

  • Lesion detection

Regression (Prediction of quantitative parameters)

  • Windowing Presets for Rendering

Image Enhancement

  • Noise Reduction
  • Multi-modal input data
  • Single-click solutions
  • No parameterizations necessary
  • Tumor segmentation in PET and SPECT
  • Seamless Integration
  • Workstation component
  • API for hassle-free integration
  • Complete Package from Annotation of the data to Integration

Work Cases

Liver and Kidney Segmentation

In this example we realized a robust and accurate segmentation of liver and kidney in various Magnetic Resonance Imaging (MRI) sequences. The apporach has proven to cope with even non-standardized image intensities and varying contrast in MRI. All aspects of a typical AI service project have been realized in-house by our annotation and software experts.

  • Validations set: 4,000 MRI slices
  • Average DICE coefficient across sequences: 91%

Chimaera Model Compression Technology

Medical applications in computer aided diagnosis like fluoroscopy require real-time capable algorithms which can process high frame rates. In the same time there is a growing demand for image processing applications on low-end hardware like mobile devices or embedded solutions.

We have developed an algorithm that in a smart way reduces the number of parameters with almost no effect on the accuracy.

We have trained and tested our algorithms with a dataset of 67 images of a seven-class segementation problem. The target organs are: right and left lung, liver, spleen, right and left kidney. This data set was also used for a two-class-segmentation problem (i.e. differentiating tissue from background). As basis for our compressed algorithm we used the SegNetmodel.

 Number of ParametersAccuracy

Reduction of Parameters in %

SegNet7-class problem


Our Solution with reduced Parameters

SegNet 7-class problem





73.2 %


SegNet  2-class problem



Our Solution with reduced Parameters

SegNet 2-class problem


95.9 %

With our algorithm we improved the frame rate by the factor 3.5 and reduced the memory usage by more than 60%. This applications enables new possibilities especially for mobile devices and real-time solutions.

Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. CoRR, abs/1511.00561, 2015.

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