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Related Experiment Video

Updated: Oct 18, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry.

Xu Xiao^1,2, Ying Qiao¹, Yudi Jiao¹

¹Department of Computer Science, School of Informatics, Xiamen University, Xiamen, China.

Frontiers in Genetics

|October 4, 2021

Summary

This summary is machine-generated.

Dice-XMBD is a novel deep learning algorithm for accurate and efficient single-cell segmentation in imaging mass cytometry (IMC) data. This tool overcomes limitations of existing methods, improving analysis of tumor microenvironments.

Keywords:

U-net digital pathology imaging mass cytometry knowledge distillation multiplexed imaging single cell segmentation

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Last Updated: Oct 18, 2025

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Area of Science:

Biomedical imaging
Computational pathology
Single-cell analysis

Background:

Highly multiplexed imaging, including Imaging Mass Cytometry (IMC), offers subcellular resolution of tumor microenvironments.
Accurate single-cell segmentation is critical for IMC data analysis but current methods are often inaccurate or labor-intensive.

Purpose of the Study:

To develop an efficient and accurate deep learning-based cell segmentation algorithm for IMC data.
To address the challenges in processing and interpreting high-resolution, multi-channel IMC images.

Main Methods:

Development of Dice-XMBD, a deep learning algorithm for cell segmentation.
Evaluation of Dice-XMBD against existing state-of-the-art methods on IMC images.
Utilized nuclear, membrane, and cytoplasm markers for segmentation.

Main Results:

Dice-XMBD achieved more accurate single-cell segmentation compared to current methods.
The algorithm demonstrated efficient processing of IMC images.
Effective segmentation was achieved across various marker types.

Conclusions:

Dice-XMBD provides a reliable and efficient solution for single-cell segmentation in IMC data.
This advancement facilitates deeper understanding of cellular composition and interactions in tumor microenvironments.
The developed algorithm and datasets are publicly available for broader research application.