3D Deep-learning-based Segmentation of Human Skin Sweat Glands and Their 3D Morphological Response to Temperature Variations
🤖 AI Summary
Current studies of eccrine gland morphology are constrained by two-dimensional, ex vivo, and destructive imaging modalities, precluding in vivo, three-dimensional (3D), dynamic quantification. To address this, we propose the first 3D Transformer-based multi-object segmentation framework integrating sliding-window processing, spatial-channel joint attention, and heterogeneous shallow-deep feature modeling, applied to optical coherence tomography (OCT) volumetric data. This enables real-time, noninvasive, high-accuracy 3D quantitative analysis of cutaneous eccrine glands. We establish the first 3D morphological benchmark for eccrine glands in healthy individuals and—uniquely—dynamically quantify subtle stimulus-induced changes in gland volume, branch count, and curvature under thermal challenge. Our approach overcomes fundamental limitations of conventional imaging, providing a novel paradigm for investigating inter-individual variability, elucidating pathophysiological mechanisms, and clinically assessing hyperhidrosis and bromhidrosis.
Application Category
📝 Abstract
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 multi-object segmentation framework, integrating a sliding window approach, joint spatial-channel attention mechanism, and architectural heterogeneity between shallow and deep layers. Our proposed network enables precise 3D sweat gland segmentation from skin volume data captured by optical coherence tomography (OCT). For the first time, subtle variations of sweat gland 3D morphology in response to temperature changes, have been visualized and quantified. Our approach establishes a benchmark for normal sweat gland morphology and provides a real-time, non-invasive tool for quantifying 3D structural parameters. This enables the study of individual variability and pathological changes in sweat gland structure, advancing dermatological research and clinical applications, including thermoregulation and bromhidrosis treatment.
Problem
Research questions and friction points this paper is trying to address.
3D segmentation of human skin sweat glands
Quantifying sweat gland morphology changes with temperature
Non-invasive real-time sweat gland analysis tool
Innovation
Methods, ideas, or system contributions that make the work stand out.
3D transformer-based multi-object segmentation framework
Sliding window approach with joint attention mechanism
OCT-based real-time non-invasive 3D quantification
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