Medical imaging involves multiple sources such as MRI, CT, X-ray, ultrasound, and PET/SPECT. Engineers and scientists must visualize and analyze multidomain image data to extract clinically meaningful information.
In this webinar, explore tools and algorithms that MATLAB® provides to support end-to-end medical imaging analysis and AI workflows, such as I/O, 3D visualization, segmentation, labeling, 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 webinar, you will learn through demonstrations how to:
Access and visualize medical images in the Medical Image Labeler
- Interactively segment lung tissue
- Create a machine learning model to characterize tissue
- Explore segmenting with the MONAI Label platform
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
Learn more:
- Get Started with Medical Imaging Toolbox: https://bit.ly/4aH2XwP
- Get Started with MONAI Label in Medical Image Labeler: https://bit.ly/4dYT5Bj
- Get Started with Radiomics: https://bit.ly/3V2tsqz
- Cellpose for Microscopy Segmentation: https://bit.ly/3Vlcghv
Chapters:
00:00 Introduction
02:34 Medical Imaging Workflow and Capabilities: Importing, Visualization, Preprocessing, Registration, Segmentation and Labeling
10:29 Demo 1: Lung Visualization, Segmentation, Labeling and Quantification using Medical Image Labeler app and MONAI
20:26 What is Radiomics?
22: 30 Demo 2: Classifying Tumors Using Radiomics
29:20 Processing Large Images and What is Cellpose
31:30 Demo 3: Processing Microscopy Images Using Blocked Images and Cellpose
30:35 Medical Imaging Workflow and Capabilities: Apps, Analysis, Deployment, V&V
42:24 - Learn More
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