CD-RCM: Generalizable Continuous-Depth Novel View Synthesis for Reflectance Confocal Microscopy
Quick Answer
The CD-RCM model introduces a novel view synthesis approach for reflectance confocal microscopy, enabling continuous-depth visualization of tissue with isotropic 3D volumes.
Quick Take
The CD-RCM model introduces a novel view synthesis approach for reflectance confocal microscopy, enabling continuous-depth visualization of tissue with isotropic 3D volumes. This method allows for arbitrary-direction sectioning without patient-specific optimization, achieving high-fidelity results in under a second of inference time.
Key Points
- CD-RCM provides continuous-depth visualization from sparse RCM z-stacks.
- Achieves isotropic 3D representation, enhancing tissue interpretation.
- Allows arbitrary-direction sectioning similar to histopathological examinations.
- High-fidelity results are generated with sub-second inference times.
- Tailored architecture accounts for RCM's unique imaging physics.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 12635v1 Announce Type: new Abstract: 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 $\mu$m) $\sim$6 times higher compared to axial resolution, which is defined by the optical sectioning (3 $\mu$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 $\mu$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.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval
A phase-aware LLM agent optimizes human-object interaction retrieval, outperforming Optuna TPE by 33.3% and VDTuner by 34.2% on the HICO-DET benchmark. This method enhances throughput by 15.3x over UniIR and demonstrates strong transferability across vector database management systems.