/tag/image analysis
MATLAB Seminar March 16 3-4PM: Medical Image Analysis and AI Workflows
Medical images come from multiple sources such as MRI, CT, X-ray, ultrasound, and PET. Analysis of these images requires a comprehensive environment for data access, visualization, processing, and algorithm development. The main challenge is to extract clinically meaningful information based on advanced techniques such as Artificial Intelligence (AI). To achieve this, one needs to clean, segment, register, and label a large collection of images. For the AI analysis, there are many more challenges such as iteratively adjusting AI models or learning parameters. MATLAB provides tools such as Medical Imaging Toolbox and Deep Learning Toolbox and algorithms for end-to-end medical image analysis and AI workflow.
Fluorescence Lifetime Imaging Microscopy in Cancer Research
Multiphoton FLIM microscopy offers many opportunities to investigate processes in live cells, tissue and animal model systems. For redox measurements, FLIM data is mostly published by cell mean values and intensity-based redox ratios. Our method is based entirely on FLIM parameters generated by 3-detector time domain microscopy capturing autofluorescent signals of NAD(P)H, FAD and novel FLIM-FRET application of Tryptophan and NAD(P)H-a2%/FAD-a1% redox ratio. Furthermore, image data is analyzed in segmented cells thresholded by 2 × 2 pixel Regions of Interest (ROIs) to separate mitochondrial oxidative phosphorylation from cytosolic glycolysis in a prostate cancer cell line. Hundreds of data points allow demonstration of heterogeneity in response to intervention, identity of cell responders to treatment, creating thereby different subpopulations.
