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.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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