Medical Imaging Workflows with MATLAB
| Start Time | End Time |
|---|---|
| 17 Nov 2025, 20:00 EST | 17 Nov 2025, 21:00 EST |
Overview
Medical images come from multiple sources such as MRI, CT, X-ray, ultrasound, and PET/SPECT. Engineers and scientists must visualize and analyse this multi-domain image data to extract clinically meaningful information.
In this seminar, explore tools and algorithms MATLAB provides for end-to-end medical image analysis and AI workflows – I/O, 3D visualization, segmentation, labelling and analysis of medical image data. Learn how to import, visualize, preprocess, register, segment, and label medical image data and train and use AI models on the data.
Highlights
In this presentation, you will learn through demonstrations how to:
- Access and visualize medical images in the medicalImageLabeler
- Interactively segment lung tissue
- Create a machine learning model to characterize tissue
- Explore segmenting with MONAI Label
- Extract and characterize regions of interest
- Create DICOM volumes
- Use radiomics features to classify tumors as benign or cancerous
- Process (huge) whole-slide images
- Block-process arbitrarily large data
- Use a pretrained deep learning model (Cellpose) to segment cells
About the Presenter
Dr Emmanuel Blanchard is a Senior Education Customer Success Engineer at MathWorks. He joined the company in 2014 as a Training Engineer and later served as an Application Engineer from 2018 to 2024, with a primary focus on data analytics. Over the years, he has delivered numerous MATLAB courses and specialized workshops on topics such as machine learning, deep learning, statistics, optimization, image processing, and parallel computing. Prior to joining MathWorks, Emmanuel was a Lecturer in Mechatronic Engineering at the University of Wollongong. He holds a Ph.D. in Mechanical Engineering from Virginia Tech and has professional experience as a Systems/Controls Engineer at Cummins Engine Company, as well as research roles at institutions in California and Virginia.
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