ZeroReg3D：用于3D连续组织病理学图像重建的零样本配准流程
ZeroReg3D: A Zero-shot Registration Pipeline for 3D Consecutive Histopathology Image Reconstruction
AI总结：
本文提出ZeroReg3D零样本配准流程，融合深度学习关键点匹配与优化型仿射、非刚性配准，实现连续病理切片的稳健3D重建。
摘要：
组织学分析在理解组织结构和病理学方面发挥着关键作用。尽管近期配准方法的进展改善了2D组织学分析，但这些方法往往难以保留关键的3D空间关系，从而限制了其在临床和研究应用中的实用性。具体而言，由于组织变形、切片伪影、成像技术的差异以及光照不一致，从2D切片构建准确的3D模型仍然具有挑战性。基于深度学习的配准方法已表现出更优的性能，但存在泛化能力有限且需要大规模训练数据的问题。相比之下，非深度学习方法具有更好的泛化能力，但往往会牺牲准确性。在本研究中，我们提出了ZeroReg3D，这是一种新颖的零样本配准流程，专为从连续组织学切片进行准确3D重建而设计。通过将基于零样本深度学习的关键点匹配与基于优化的仿射和非刚性配准技术相结合，ZeroReg3D有效应对了组织变形、切片伪影、染色差异和光照不一致等关键挑战，且无需重新训练或微调。代码已公开发布于https://github.com/hrlblab/ZeroReg3D。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
