Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry

This protocol describes Deepometry, an open-source application for supervised and weakly supervised deep learning analysis of imaging flow cytometry datasets. The protocol provides runtime scripts for Python, MATLAB and a standalone application. Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model.

Automated summary

This protocol describes an open-source application called Deepometry for supervised and weakly supervised deep learning analysis of imaging flow cytometry datasets. It explains the application of deep learning to single-cell images for supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. The protocol provides runtime scripts for Python, MATLAB, and a standalone application, allowing for flexible and user-friendly environment for morphological phenotyping and deep learning feature exploration.