Introduction to ultrack, Cell Tracking in Python Under Segmentation Uncertainty

Introduction to ultrack, Cell Tracking in Python Under Segmentation Uncertainty | Jordão Bragantini | October 19th, Halfway to I2K 2023 Authors: Jordão Bragantini, Chan Zuckerberg Biohub Description: Cell tracking performance is highly dependent on the input segments' accuracy. In this tutorial, we will introduce ultrack. A novel multi-hypothesis tracking framework that supports several kinds of inputs from intensity images, segmentation masks, contours, or a combination of them. It can process from small 2D datasets to large multi-terabyte 3D timelapse, in any numpy compatible data format (e.g. dask, zarr arrays). The workshop will start with a description of the principles behind ultrack and finish with a hands-on tutorial using Google Colab or a local Jupyter notebook. Pre-Workshop Instructions: Have your laptop ready with conda or your favorite virtual environment setup. If that's not possible the interactive session can be followed through Zoom & Google Colab. Keywords: image analysis, machine learning, segmentation, cell tracking, python