Juan Nunez-Iglesias
I'm a research scientist helping other scientists get insights from their image data using Python. I've been using Python since 2008, and the main scientific Python ecosystem (NumPy, SciPy, & co) since 2010. In 2012, on a whim, I went to my first SciPy (US) conference, and it changed my life! I realised that "open source" didn't mean just posting the code online. It meant actively collaborating on code with other scientists, across vast distances and at different times. Before you could say "import numpy as np", I had joined the scikit-image team, written a paper about it, written a whole book on SciPy (!), started new collaborative, open source libraries, and just generally been all-in on Scientific Python. I've been coming back to SciPy as often as I can to pay it forward for new folks in our community! 😊
Sessions
Between telescopes and satellite cameras and MRI machines and microscopes, scientists are producing more images than they can realistically look at. They need specialized viewers for multi-dimensional images, and automated tools to help process those images into knowledge. In this tutorial, we will cover the fundamentals of algorithmic image analysis, starting with how to think of images as NumPy arrays, moving on to basic image filtering, and finishing with a complete workflow: segmenting a 3D image into regions and making measurements on those regions. At every step, we will visualize and understand our work using matplotlib and napari.
napari is an n-dimensional image viewer for Python. If you’ve ever tried plt.imshow(arr) and made Matplotlib unhappy because arr has more than two dimensions, then napari might be for you! napari will gladly display higher-dimensional arrays by providing sliders to explore additional dimensions. But napari can also: overlay derived data, such as points, segmentations, polygons, surfaces, and more; and annotate and edit these data, using standard data structures like NumPy or Zarr arrays, allowing you to seamlessly weave exploration, computation, and annotation in image analysis.