用3D深度学习精准分割汗腺，实时观察温度变化下的结构动态。
3D Deep-learning-based Segmentation of Human Skin Sweat Glands and Their 3D Morphological Response to Temperature Variations
- 基于3D Transformer的多对象分割框架，融合滑窗与注意力机制。
- 首次实现汗腺三维形态对温变响应的可视化与量化分析。
- 适用于皮肤病理研究和汗腺功能异常的非侵入式检测。
皮肤作为热交换的主要调节器官，依赖汗腺进行体温调控。汗腺形态改变在多种病理状态和临床诊断中具有重要意义。现有观测方法受限于二维、体外及破坏性，亟需实时、非侵入、可量化的技术。本文提出一种基于3D Transformer的多对象分割框架，结合滑窗策略、空间-通道联合注意力机制及浅层与深层网络的架构异质性，实现从光学相干断层扫描（OCT）获取的皮肤体积数据中精确分割汗腺。首次实现了汗腺三维形态随温度变化的细微响应的可视化与量化。该方法建立了正常汗腺形态基准，提供了一种实时、非侵入的三维结构参数量化工具，有助于研究个体差异及病理变化，推动皮肤病学研究与临床应用，包括体温调节及腋臭治疗。
原文摘要 · Abstract (English)
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.
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