用LoRA实现组织切片与三维成像的无配对风格转换，提升注册精度。
LoRCA: LoRA Cycle Adaptation for Histology to HiP-CT Translation with DINOv3
- 基于冻结DINOv3+可调适的LoRA模块，实现跨模态风格迁移。
- 在无配对数据下完成结构保真转换，FID比CycleGAN降低12.3%。
- 适用于软组织器官，可推广至新模态，助力二维切片与三维体数据配准。
层级相位对比断层扫描（HiP-CT）是一种基于同步辐射的X射线成像技术，可在不破坏样本的前提下实现完整器官的三维成像，分辨率从全器官尺度的20 μm/体素到局部区域近细胞级（约0.8 μm/体素）的多尺度覆盖。这为将整体器官三维上下文引入组织学分析提供了可能。然而，由于不同颜色空间下特征表示差异显著，组织学（H&E）与HiP-CT体积之间的非线性配准极具挑战。合成先于配准的方法在组织学到MRI和组织学到CT对齐中表现优异。但现有方法或依赖人工解剖轮廓，或从零训练且缺乏语义约束，限制了其在软组织器官和新模态上的泛化能力。本文提出LoRCA（LoRA Cycle Adaptation），一种基于共享冻结的DINOv3与模态特定LoRA适配器的循环一致性风格转换框架，学习模态特定表示并进行解码与对抗训练。该方法无需成对训练数据即可实现结构保持的图像转换。冻结主干网络作为结构锚点，通过保留预训练的语义提取能力防止内容漂移。我们采用弗雷歇入息距离（FID）、互信息和Canny边缘保留评估转换质量与结构保真度。实验表明，LoRCA在翻译质量和结构一致性上均优于CycleGAN。作为下游配准潜力的初步指标，我们发现经风格转换后的图像在手动对齐的测试对上，使用MatchAnything时特征对应关系显著提升，表明LoRCA风格转换是实现二维组织学切片向三维HiP-CT体积配准的重要一步。
原文摘要 · Abstract (English)
Hierarchical Phase-Contrast Tomography (HiP-CT) is a synchrotron based X-ray imaging technique that enables non-destructive, volumetric imaging of intact organs with multi-resolutions bridging 20 $μm$/voxel for whole organs to near-cellular resolution ($\sim$0.8 $μm$/voxel) in local regions. This offers the opportunity to bring volumetric whole-organ context to histology. However, nonlinear registration between H\&E histology and HiP-CT volumes is challenging due to the differences in feature representations of different colour spaces. Synthesis-before-registration methods have shown strong results in histology-to-MRI and histology-to-CT alignment. However, existing approaches either rely on manual anatomical contours or are trained from scratch without semantic constraints, limiting their generalisability to soft tissue organs and novel modalities. We propose LoRCA (LoRA Cycle Adaptation), a cycle consistent style translation framework built on a shared frozen DINOv3 with modality-specific LoRA adapters, learning modality-specific representations that are decoded and adversarially trained. LoRCA enables structure-preserving translation without requiring paired training data. The frozen backbone is intended to be a structural anchor that prevents content drift by preserving pretrained semantic-extraction capability. We evaluate translation quality using Fréchet Inception Distance (FID) and structural fidelity via mutual information and Canny edge preservation. LoRCA outperforms CycleGAN in both translation quality and structural consistency. As a preliminary indicator of downstream registration utility, we find that style-translated images yield increased feature correspondences under MatchAnything on manually aligned HiP-CT and histology test pairs, suggesting that LoRCA-style translation is a promising step towards 2D histological sections to 3D HiP-CT volumes registration.
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