发表机构
University of California, Santa Cruz; University of California, Berkeley（加利福尼亚大学圣克鲁兹分校; 加利福尼亚大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对皮肤病学实践中常用2D方法的现状，提出DermDepth单视图度量尺度3D模型及D-Synth数据集，经实验训练和微调，能有效校正度量尺度误差，在多基准通用并与医学文献测量结果一致。
AI 中文摘要
皮肤病学实践通常涉及测量和跟踪病变大小、形态和纹理，这是伤口或皮肤癌筛查、监测和诊断的关键组成部分。目前常用现成相机传感器对皮肤表面成像，导致研究多聚焦于2D方法，而这些目标自然受益于3D信息。本文展示了无需额外硬件或多次捕获，就能实现皮肤镜和宏观病例的密集单目3D重建、度量尺度测量和丰富表面法线纹理估计。提出了DermDepth，首个用于皮肤病学领域的单视图度量尺度3D模型以及D-Synth，首个具有像素完美3D信息的合成皮肤镜数据集。实验表明，在D-Synth上训练DermDepth可将真实皮肤镜数据的度量尺度误差从超过16倍校正到低于1.1倍，同时保持几何质量并增加纹理丰富度。在少量真实临床样本上微调可使方法在跨越几毫米到几百厘米范围、不同肤色、慢性伤口病例的三个真实世界基准上通用，并产生与医学文献中报道的疾病大小大致一致的测量结果。所有代码、数据和模型可在指定网址获取。
英文摘要
Dermatological practice routinely involves measuring and tracking lesion size, morphology and texture, as critical components of wound or skin cancer screening, monitoring and diagnosis. To accomplish this task, practitioners often image the skin surface with commonly available off-the-shelf camera sensors. This has led to an overwhelming research focus on 2D methods while these objectives naturally benefit from 3D information. In this paper, we demonstrate that dense monocular 3D reconstructions, metric scale measurements and rich surface normal texture estimates are achievable for both dermoscopic and macroscopic cases without the need for additional hardware or multiple captures. We present DermDepth, the first single-view metric scale 3D model for the dermatological domain and D-Synth, the first synthetic dermoscopic dataset with pixel-perfect 3D information. Our experiments show training DermDepth on D-Synth corrects metric scale error from over 16x to under 1.1x for real dermoscopic data, while preserving geometric quality and increasing texture richness. Fine-tuning on a small amount of real clinical samples generalizes our method across three real-world benchmarks spanning the few mm to hundred cm range, diverse skin-tones, chronic wound cases and produces measurements broadly consistent with disease size reported in medical literature. All code, data and models are available at https://github.com/hectorcarrion/dermdepth.
CommentsAccepted at MICCAI 2026
