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.

翻译：反射共聚焦显微镜（RCM）通过获取连续深度处的共面图像，形成稀疏的z轴堆栈，能够对人体皮肤进行无创的细胞分辨率“光学活检”。受光学限制，这些堆栈是各向异性的三维体积，其横向分辨率（0.5 μm）比轴向分辨率（由光学切片（3 μm）定义）高约6倍，这限制了组织解读。我们的目标是插入中间切片使三维体积各向同性，从而实现连续深度可视化。这种表示允许任意方向切片，包括类似组织病理学的横截面检查，而无需针对患者进行优化。为此，我们首次提出面向RCM的新视角合成（NVS）方法——CD-RCM，这是一种前馈模型，能从稀疏采样的RCM堆栈预测未见过的真实深度。经典的神经渲染方法专注于从表面级多视角观测进行重建。与表面级相机视角不同，RCM能获取组织表面以下直至200 μm的光学切片共面图像。但在RCM堆栈可视化中，浅层（朝向表面）的观测会遮挡深层观测。这种独特的轴向成像几何和逐层解剖结构促使我们开发了定制的架构与训练框架，该框架显式建模了RCM深度分辨的、具有遮挡性的成像物理特性。实验表明，CD-RCM能在亚秒级推理时间内实现高保真度新视角合成。