ZeroReg3D: A Zero-shot Registration Pipeline for 3D Consecutive Histopathology Image Reconstruction
Journal:
arXiv
Published Date:
Jun 27, 2025
Abstract
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
