ZeroReg3D: a zero-shot registration pipeline for 3D consecutive histopathology image reconstruction.
Academic Article
Overview
abstract
PURPOSE: Histological analysis plays a crucial role in understanding tissue structure and pathology. Although 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. APPROACH: We introduce ZeroReg3D, a zero-shot registration pipeline that integrates zero-shot deep learning-based keypoint matching and non-deep-learning registration techniques to effectively mitigate deformation and sectioning artifacts without requiring extensive training data. RESULTS: ∼ CONCLUSIONS: We introduced ZeroReg3D, a 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.
