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Deepometry, A Framework For Applying Supervised And Weakly Supervised Deep Learning To Imaging Cytometry.

Home
Deepometry, A Framework For Applying Supervised And Weakly Supervised Deep Learning To Imaging Cytometry.

Related Experiment Video

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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

Minh Doan^1,2, Claire Barnes³, Claire McQuin⁴

¹Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA. minh.x.doan@gsk.com.

Nature Protocols

|June 19, 2021

View abstract on PubMed

Summary

This summary is machine-generated.

This protocol uses deep learning for cell classification from imaging flow cytometry data. It enables morphological phenotyping with supervised and weakly supervised learning methods.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Biotechnology
Computational Biology
Data Science

Background:

Imaging flow cytometry generates complex single-cell image data.
Extracting detailed morphological information from these images is challenging.
Deep learning (DL) presents a powerful approach for advanced image analysis.

Purpose of the Study:

To describe a protocol for applying deep learning to single-cell images from imaging flow cytometry.
To enable supervised cell classification and weakly supervised learning for morphological phenotyping.
To provide open-source tools for data acquisition, transformation, DL training, and inference.

Main Methods:

Utilizing deep learning for supervised and weakly supervised classification of single-cell images.

Acquiring and transforming input data for deep learning models.

Implementing training and inference using an open-source web-based application with Python and MATLAB scripts.

Employing multi-dimensional visualization tools for exploring deep learning features.

Main Results:

A comprehensive protocol for deep learning-based morphological phenotyping is presented.
The protocol supports both supervised and weakly supervised learning approaches.
Open-source scripts and interfaces facilitate flexible implementation and exploration.

Conclusions:

Deep learning significantly enhances the extraction of morphological features from imaging flow cytometry data.
This protocol offers a user-friendly environment for advanced single-cell image analysis.
The methods enable flexible and powerful morphological phenotyping and feature exploration.