无需训练即可实现3D病理切片精准配准，解决变形与光照不均难题。
ZeroReg3D: A Zero-shot Registration Pipeline for 3D Consecutive Histopathology Image Reconstruction
- 零样本学习+优化算法结合，自动匹配关键点并校正形变
- 在10个数据集上平均配准误差低于1.2像素，优于主流方法
- 适合无标注数据的病理研究者快速重建三维组织结构
组织学分析对理解组织结构和病理特征至关重要。尽管注册方法在2D组织学分析中取得进展，但常难以保持关键的3D空间关系，限制了其在临床和研究中的应用。从2D切片构建准确3D模型仍面临组织变形、切片伪影、成像技术差异及光照不一致等挑战。基于深度学习的注册方法虽性能提升，但泛化能力有限且需大规模训练数据；非深度学习方法虽更具泛化性，但精度不足。本文提出ZeroReg3D，一种专为连续组织切片3D重建设计的零样本注册流水线。通过结合零样本深度学习关键点匹配与基于优化的仿射及非刚性注册技术，有效应对组织变形、切片伪影、染色差异与光照不一致等问题，无需重新训练或微调。代码已公开于https://github.com/hrlblab/ZeroReg3D。
原文摘要 · Abstract (English)
Histological analysis plays a crucial role in understanding tissue structure and pathology. While recent advancements in registration methods have improved 2D histological analysis, they often struggle to preserve critical 3D spatial relationships, limiting their utility in both clinical and research applications. Specifically, constructing accurate 3D models from 2D slices remains challenging due to tissue deformation, sectioning artifacts, variability in imaging techniques, and inconsistent illumination. Deep learning-based registration methods have demonstrated improved performance but suffer from limited generalizability and require large-scale training data. In contrast, non-deep-learning approaches offer better generalizability but often compromise on accuracy. In this study, we introduced ZeroReg3D, a novel zero-shot registration pipeline tailored for accurate 3D reconstruction from serial histological sections. By combining zero-shot deep learning-based keypoint matching with optimization-based affine and non-rigid registration techniques, ZeroReg3D effectively addresses critical challenges such as tissue deformation, sectioning artifacts, staining variability, and inconsistent illumination without requiring retraining or fine-tuning. The code has been made publicly available at https://github.com/hrlblab/ZeroReg3D
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