Deep learning has demonstrated astonishing segmentation results in microscopy, outperforming all existing approaches. While many codes are publicly available, they require expertise that most biologists lack. The goal of this workshop is to learn how to train and process deep convolutional neural networks for image segmentation through a comprehensive image analysis pipeline for immune profiling of 2D multiplexed images. More specifically, participants will learn how to install python packages and run Jupyter notebooks, use the ImageJ plugin Annotater to manually annotate images, train deep learning classifiers and use them to segment tissue and nuclei, identify cell markers, batch process images and analyze them with an R script. This workshop does not require proficiency in any coding language.
This is a 3-day workshop. Pdf describing the background required to understand the methods and links to video tutorials are available in Courses. All the codes and data used during the workshop are located in Codes and Data.
Please cite our paper if you use our codes:
Thierry Pécot, Alexander Alekseyenko and Kristin Wallace (2022): A deep learning segmentation strategy that minimizes the amount of manually annotated images
Thierry Pécot, Maria C. Cuitiño, Roger H. Johnson, Cynthia Timmers, Gustavo Leone (2022): Deep learning tools and modeling to estimate the temporal expression of cell cycle proteins from 2D still images