3D Deep-learning-based Segmentation of Human Skin Sweat Glands and Their 3D Morphological Response to Temperature Variations

Skin, the primary regulator of heat exchange, relies on sweat glands for thermoregulation. Alterations in sweat gland morphology play a crucial role in various pathological conditions and clinical diagnoses. Current methods for observing sweat gland morphology are limited by their two-dimensional, in vitro, and destructive nature, underscoring the urgent need for real-time, non-invasive, quantifiable technologies. We proposed a novel three-dimensional (3D) transformer-based segmentation framework, enabling quite precise 3D sweat gland segmentation from skin volume data captured by optical coherence tomography (OCT). We quantitatively reveal, for the first time, 3D sweat gland morphological changes with temperature: for instance, volume, surface area, and length increase by 42.0%, 26.4%, and 12.8% at 43°C vs. 10°C (all p < 0.001), while S/V ratio decreases (p = 0.01). By establishing a benchmark for normal sweat gland morphology and offering a real-time, non-invasive tool for quantifying 3D structural parameters, our approach facilitates the study of individual variability and pathological changes in sweat gland morphology, contributing to advancements in dermatological research and clinical applications.