RegWSI：结合深度特征与基于强度的方法实现全切片图像配准：ACROBAT 2023挑战赛冠军
RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge
AI总结：
本研究提出名为RegWSI的两步混合全切片图像配准方法，结合深度特征初始对齐与基于强度的非刚性配准，无需微调即可适配多类组织与染色，获ACROBAT 2023挑战赛冠军，在多个公开数据集上表现优异，已整合进DeeperHistReg框架，推动数字病理发展。
摘要：
不同染色全切片图像（WSI）的自动配准对于融合不同可见结构提供的互补信息以提升诊断和预后效果至关重要，也可用于在连续切片或重染切片之间快速转移标注，从而大幅减少标注时间及相关成本。然而，不同染色的玻片制备过程存在差异，且组织会发生复杂的大形变。因此，科研界和专攻数字病理的医院迫切需要一种鲁棒、高效且精准的配准方法。\n我们提出了一种两步混合方法，包括：（i）基于深度学习和特征的初始对齐算法；（ii）采用实例优化的基于强度的非刚性配准。该方法无需针对特定数据集进行任何微调，可直接应用于任意所需的组织类型和染色类型，在ACROBAT 2023挑战赛中获得第一名。\n我们使用三个公开数据集进行了评估：（i）ANHIR、（ii）ACROBAT、（iii）HyReCo，并针对配准所用分辨率以及初始对齐的鲁棒性和稳定性开展了多项消融研究。该方法在ACROBAT数据集上取得了最精准的结果，在HyReCo数据集的重染切片上达到细胞级配准精度，在ANHIR数据集的评估中也跻身最优方法行列。该方法无需针对新数据集微调，可直接用于其他类型的显微图像，已被整合到DeeperHistReg框架中，支持用户直接在任意所需金字塔层级对WSI进行配准、变换和保存。该方法是WSI配准领域的一项重要贡献，推动了数字病理领域的发展。
英文摘要：
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.
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