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Related Experiment Video

Updated: Oct 28, 2025

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

Automatic segmentation of skin cells in multiphoton data using multi-stage merging.

Philipp Prinke¹, Jens Haueisen², Sascha Klee^2,3

¹Institute for Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693, Ilmenau, Germany. philipp.prinke@tu-ilmenau.de.

Scientific Reports

|July 16, 2021

Summary

A new algorithm automatically segments human skin cells in 3D multiphoton tomography data. This robust method enhances cell classification for potential skin cancer detection.

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Published on: September 22, 2013

Area of Science:

Biomedical Imaging
Computational Biology
Dermatology

Background:

Accurate segmentation of human skin cells in 3D multiphoton tomography data is crucial for diagnostic applications.
Existing methods often struggle with depth-dependent variations in contrast and cell size, limiting robustness.

Purpose of the Study:

To develop a novel, robust automatic segmentation algorithm for human skin cell components (cytoplasm and nuclei) in 3D multiphoton tomography data.
To introduce new features for improved cell classification and to enable automated analysis for potential skin cancer detection.

Main Methods:

A multi-stage superpixel merging approach was employed to overcome limitations of global thresholds and handle depth-dependent data characteristics.
A cell model utilizing four features, including two novel metrics (Outer Cell Inner Nucleus relationship and stability index), was developed for fuzzy classification.
The algorithm was validated on a 3D image stack of human skin layers (stratum spinosum and stratum basale).

Main Results:

The proposed algorithm demonstrated robust segmentation of skin cell components, independent of empirical global thresholds.
The novel features (OCIN and stability index) combined with existing ones improved the model-based fuzzy evaluation of cell segments.
Successful application on a 3D image dataset of healthy human skin confirmed the pipeline's efficacy.

Conclusions:

The developed image processing pipeline enables fully automated classification of human skin cells in multiphoton data.
This approach provides a foundational tool for non-invasive optical biopsy and the early detection of skin cancer.