The automatic registration of differently stained whole slide images (WSIs)
is crucial for improving diagnosis and prognosis by fusing complementary
information emerging from different visible structures. It is also useful to
quickly transfer annotations between consecutive or restained slides, thus
significantly reducing the annotation time and associated costs. Nevertheless,
the slide preparation is different for each stain and the tissue undergoes
complex and large deformations. Therefore, a robust, efficient, and accurate
registration method is highly desired by the scientific community and hospitals
specializing in digital pathology. We propose a two-step hybrid method
consisting of (i) deep learning- and feature-based initial alignment algorithm,
and (ii) intensity-based nonrigid registration using the instance optimization.
The proposed method does not require any fine-tuning to a particular dataset
and can be used directly for any desired tissue type and stain. The method
scored 1st place in the ACROBAT 2023 challenge. We evaluated using three open
datasets: (i) ANHIR, (ii) ACROBAT, and (iii) HyReCo, and performed several
ablation studies concerning the resolution used for registration and the
initial alignment robustness and stability. The method achieves the most
accurate results for the ACROBAT dataset, the cell-level registration accuracy
for the restained slides from the HyReCo dataset, and is among the best methods
evaluated on the ANHIR dataset. The method does not require any fine-tuning to
a new datasets and can be used out-of-the-box for other types of microscopic
images. The method is incorporated into the DeeperHistReg framework, allowing
others to directly use it to register, transform, and save the WSIs at any
desired pyramid level. The proposed method is a significant contribution to the
WSI registration, thus advancing the field of digital pathology.

我们提出了一个两步骤混合方法，包括深度学习和特征提取的初始对齐算法以及基于强度的非刚性配准。该方法不需要对特定数据集进行微调，可直接用于任何所需的组织类型和染色剂。该方法在 ACROBAT 数据集上获得了最准确的结果，在 HyReCo 数据集上实现了细胞级重染的配准精度，同时在 ANHIR 数据集上也表现出色。该方法不需要对新数据集进行微调，并可直接用于其他类型的显微镜图像。该方法已集成到 DeeperHistReg 框架中，可供其他人直接使用，以在任何所需的金字塔级别上对 WSIs 进行配准、变换和保存。该方法对 WSI 的配准是一个重要的贡献，推动了数字病理学领域的发展。