让皮肤显微图像实现任意深度切片，无需逐个患者调参。
CD-RCM: Generalizable Continuous-Depth Novel View Synthesis for Reflectance Confocal Microscopy
- 基于稀疏深度层构建连续深度图像，突破传统光学限制。
- 生成图像保真度高，单次推理时间小于1秒。
- 专为皮肤共聚焦显微成像设计，适合病理分析与临床应用。
反射共聚焦显微镜（RCM）通过在不同深度获取横向图像，形成稀疏的深度堆叠，实现对活体皮肤的细胞级‘光学活检’。由于光学限制，这些堆叠是各向异性的三维体数据，横向分辨率（0.5 μm）约为轴向分辨率（3 μm，由光学切片决定）的6倍，限制了组织结构的解读。本文目标是通过插值中间层面，实现连续深度可视化，使三维体积各向同性化，支持任意方向截面分析（如类似组织病理学的横切面），且无需针对每位患者进行优化。为此，我们提出首个专为RCM设计的新型视图合成方法——CD-RCM，一种前馈模型，可从稀疏采样的RCM堆叠中预测真实、未观测到的深度。传统神经渲染方法侧重于从表面多视角观测重建，而RCM能获取表层以下最多200 μm深度的光学切片图像。然而在可视化过程中，浅层图像会遮挡深层结构。这一独特的轴向成像几何和分层解剖结构促使我们开发了专门的架构与训练框架，显式建模了RCM的深度分辨、遮挡成像物理特性。实验表明，CD-RCM实现了高质量的新型视图合成，推理时间低于1秒。
原文摘要 · Abstract (English)
Reflectance confocal microscopy (RCM) provides noninvasive, cellular-resolution "optical biopsies" of human skin \emph{in vivo} by acquiring en-face images at successive depths, forming a sparse z-stack. Due to optical limitations, these stacks are anisotropic 3D volumes with lateral resolution (0.5 $μ$m) $\sim$6 times higher compared to axial resolution, which is defined by the optical sectioning (3 $μ$m), limiting the interpretation of tissue. Our goal is to provide continuous-depth visualization by interpolating intermediate sections and making the 3D volume isotropic. Such a representation permits arbitrary-direction sectioning, including histopathology-like cross-sectional examination, without requiring per-patient optimization. To that end, we introduce the first RCM-specific novel-view synthesis (NVS) approach, CD-RCM, a feedforward model that predicts realistic, unseen depths from sparsely sampled RCM stacks. Classical neural rendering methods focus on reconstruction from surface-level multi-view observations. In contrast to surface-level camera views, RCM can acquire optically sectioned en-face images of tissue beyond the surface up to 200 $μ$m. However, during visualization of the RCM stacks, observations of the shallower sections (towards the surface) obscure the deeper ones. This unique axial imaging geometry and layer-dependent anatomical organization motivated our development of a tailored architectural and training framework that explicitly accounts for RCM's depth-resolved, occlusive imaging physics. Experiments demonstrate that CD-RCM achieves high-fidelity novel-view synthesis with sub-second inference time.
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