Subcutaneous tissue structural feature identification using unsupervised machine learning

Area of Science:

• Biomedical Engineering
• Computational Biology
• Dermatology

Background:

• Accurate quantification of subcutaneous (SC) tissue structure is crucial for understanding skin physiology and developing computational models.
• Manual image segmentation for SC tissue is labor-intensive, user-dependent, and lacks reproducibility.
• Robust automated algorithms for SC tissue structure identification are currently unavailable, and supervised machine learning (ML) methods require extensive labeled datasets.

Purpose of the Study:

• To present a novel unsupervised machine learning methodology for automated identification of SC tissue structural features from stained histology slides.
• To address the lack of labeled datasets for SC tissue analysis, a common limitation for supervised ML approaches.

Main Methods:

• Developed a novel 2D image transformation to generate proximal intensity maps, representing radial intensity values for each pixel.
• Reduced the proximal intensity map into a lower-dimensional feature vector space.
• Employed K-means clustering for pixel classification based on computed feature vectors, utilizing an objective method for optimal search radius selection.

Main Results:

• Successfully demonstrated the automated and robust classification and identification of the collagenous network within adipose tissue spaces in porcine skin SC tissue samples.
• The proximal intensity map and feature space reduction enabled effective clustering of SC tissue structures.
• An objective basis for selecting the optimal search radius was established for noise minimization and feature separation.

Conclusions:

• The presented unsupervised ML method offers a novel approach for automatically identifying SC tissue structures.
• This advancement aids in understanding skin physiology and developing improved in vitro tissue models.
• The methodology provides a reproducible and efficient alternative to manual segmentation for SC tissue analysis.