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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Unsupervised cell identification on multidimensional X-ray fluorescence datasets.

Siwei Wang¹, Jesse Ward², Sven Leyffer¹

¹Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA.

Journal of Synchrotron Radiation

|April 26, 2014

Summary

This summary is machine-generated.

This study introduces a new method for identifying cell structures using X-ray fluorescence microscopy. The approach accurately identifies cells, even overlapping ones, without needing prior training data.

Keywords:

X-ray fluorescence microscopy (XFM)cell identification modeling overlapping cells trace element distributions unsupervised object recognition

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Area of Science:

Cell biology
Microscopy techniques
Elemental analysis

Background:

X-ray fluorescence microscopy (XFM) generates complex datasets.
Manual analysis of XFM data is challenging and time-consuming.
Accurate identification of cellular structures is crucial for biological research.

Purpose of the Study:

To develop a novel, automated approach for locating and identifying cell structures using XFM data.
To create a robust framework for analyzing complex XFM datasets without explicit annotation.
To enable consistent identification of cellular regions, including overlapping cells.

Main Methods:

Utilizing elemental content data from X-ray fluorescence microscopy.
Employing an initialization strategy with prototypical cell regions.
Developing an algorithm for consistent identification and refinement of cell positions and areas.
Testing robustness against variations in measurements and initializations.

Main Results:

Successful identification of whole cells, including overlapping ones, without supervised training.
Demonstrated robustness to different measurements on the same sample.
Showcased consistency across different initializations.
Provided a versatile framework for complex XFM data analysis.

Conclusions:

The novel approach offers a powerful tool for analyzing complex XFM data.
This method overcomes limitations of manual analysis and traditional automated methods.
The framework is adaptable for identifying various cellular structures in intricate biological samples.