June 9, 2021 | Mimics Innovation Course | English

Webinar on Automating and Optimizing Your Workflow

Medical image data serves as a powerful information base for doctors, engineers, and researchers who are looking for solutions to improve medical care. The Mimics Innovation Suite was developed with the goal of processing medical image data as easily and efficiently as possible. 

In this virtual course, you will learn how to speed-up and optimize your workflow thanks to AI-enabled* tools, custom plugins and scripting (Python). 

Mimics Innovation Course

Intended Audience

This training is designed for engineers from medical technology companies, hospitals and for scientists from universities. 

Learning goals 

  • Automate your workflow to get the results you faster, with less manual work, less human error and less repetitive steps  

  • Drastically speed up your processes and increase consistency through a dedicated user-friendly interface   

  • Step-by-step approach on how to start with scripting and best practices 

 

Practical info

  • Mimics Innovation Course - Digital event
  • Wednesday June 9, 2021 | 3:00 PM - 5:00PM CET  
  • Language: English

 

Price

  • Free

 

Program

  • The link between MIS and Automation
  • Introduction to custom plugins   
  • AI-enabled segmentation in the cloud  
  • Scripting 
  • Q&A 

Trainers information

Marnic Jacobs
Application Specialist
at Materialise Medical

Simon Lejaegere
Product Manager Custom Plugins 
at Materialise Medical

Arsham Khayatpoor
Application Engineer
at Materialise Medical

“Researching scoliosis with the spine in an upright position is extremely challenging. The spine has many vertebrae and between each are very complex joints. Achieving very precise segmentation was only possible with the Mimics Innovation Suite, as the software was able to recognize the contours of the CT slices.”
- Dr. Saša Ćuković, Postdoctoral Research Fellow in Biomechanics, ETH Zurich

 

*AI-enabled refers to numerical algorithms using artificial intelligence including thresholding, machine learning, deep learning, graph-based & modelbased segmentation algorithms and combinations thereof which have been locked and validated before being released. It does not refer to any form of adaptive AI/ML 

 

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