@article{IR,
      author = {Bronstein, Hannah S. and Kelly, Felice D. and Jenkins,  Brian and Petrie, Stefanie K.},
      url = {http://digitalcollections.ohsu.edu/record/41298},
      title = {Machine learning tools for image analysis at the USR  Advanced Light Microscopy Core.},
      publisher = {Oregon Health and Science University},
      abstract = {Recent analysis advances include user-friendly AI that can  simplify or automate many previously manual image analysis  tasks. Here we will discuss new tools for 2D and 3D data  analysis in the ALMC and demonstrate how they make image  analysis more accurate and efficient for OHSU researchers.   Intellesis machine learning performs challenging 2D  segmentation of fluorescence and brightfield data. This  segmentation forms the foundation of many downstream  analyses, such as counting objects or assessing  fluorescence intensity within specific structures.  Intensity-based segmentation struggles with images that  have high background or high signal variability. Intellesis  machine learning can be trained to identify objects by  iterative user-guided pixel painting on a few images. This  supervised learning trains the neural net to discriminate  between background and object based on a large set of  parameters, not just local pixel intensity. We will present  examples of machine learning segmentation on fluorescence  images, colorimetric staining, and transmitted light images  from users at the ALMC.},
      number = {IR},
      doi = {https://doi.org/10.6083/bpxhc41298},
      recid = {41298},
      address = {2023-07-19},
}