Using simple machine learning for image segmentation

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Presentation transcript:

Using simple machine learning for image segmentation

A simple technique: Finding Objects (Cells) by Thresholding

Part 1: Using labeled data to find optimal intensity threshold intensity map region labels Van Valen, D. A., Kudo, T., Lane, K. M., Macklin, D. N., Quach, N. T., DeFelice, M. M., … Covert, M. W. (2016). Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments. PLOS Computational Biology, 12(11), e1005177. https://doi.org/10.1371/journal.pcbi.1005177

Inherent limits to intensity thresholding pixel intensities classified image (optimal threshold)

Image filters: ways to extract additional info about structure of images Laplace filter filtered image

Two axes are better than one Adding second feature makes it easier to separate the two classes ML algorithms can generalize to an arbitrary number of features Laplace intensity

Two axes are better than one ground truth classified (random forest) classified (with Laplace) classified (threshold only)