Medical Image Analysis and AI Workflows in MATLAB
Medical images come from multiple sources such as MRI, CT, X-ray, ultrasound, and PET/SPECT. The challenge is to visualize and analyze this multi-domain image data to extract clinically meaningful information and conduct other tasks such as training AI models.
MATLAB provides tools and algorithms for end-to-end medical image analysis and AI workflows – I/O, 3D visualization, segmentation, labeling and analysis of medical image data. This webinar shows the complete medical image analysis workflow for AI applications. You will learn how to import visualize, segment and label medical image data and utilize these data in AI model training.
- Importing and visualizing multi-domain DICOM medical images
- Segmenting and labeling 2D and 3D radiology images
- Designing and training AI and deep learning models
Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.
About the Presenter
Renee Qian is an Application Engineer supporting the Medical Devices Industry in Data Analytics and Technical Computing applications. She works closely with engineers and researchers in the biomedical community to understand and address the unique challenges and needs in this industry. Renee graduated Northwestern University with an M.S. in Biomedical Engineering. Her research was in medical imaging focusing on quantitative cerebrovascular perfusion MRI of the brain for stroke prevention. She joined the MathWorks in 2012 helping customers with MATLAB, analysis, and graphics challenges, and later transferred to Application Engineering where she specialized in Test and Measurement applications before transitioning to her current role.