AGH University of Science and Technology, Department of Measurement and Electronics, al. Mickiewicza 30, PL30059 Cracow, Poland.
Phys Med Biol. 2021 Jan 26;66(2):025006. doi: 10.1088/1361-6560/abcad7.
The use of multiple dyes during histological sample preparation can reveal distinct tissue properties. However, since the slide preparation differs for each dye, the tissue slides are being deformed and a nonrigid registration is required before further processing. The registration of histology images is complicated because of: (i) a high resolution of histology images, (ii) complex, large, nonrigid deformations, (iii) difference in the appearance and partially missing data due to the use of multiple dyes. In this work, we propose a multistep, automatic, nonrigid image registration method dedicated to histology samples acquired with multiple stains. The proposed method consists of a feature-based affine registration, an exhaustive rotation alignment, an iterative, intensity-based affine registration, and a nonrigid alignment based on modality independent neighbourhood descriptor coupled with the Demons algorithm. A dedicated failure detection mechanism is proposed to make the method fully automatic, without the necessity of any manual interaction. The described method was proposed by the AGH team during the Automatic Non-rigid Histological Image Registration (ANHIR) challenge. The ANHIR dataset consists of 481 image pairs annotated by histology experts. Moreover, the ANHIR challenge submissions were evaluated using an independent, server-side evaluation tool. The main evaluation criteria was the target registration error normalized by the image diagonal. The median of median target registration error is below 0.19%. The proposed method is currently the second-best in terms of the average ranking of median target registration error, without statistically significant differences compared to the top-ranked method. We provide an open access to the method software and used parameters, making the results fully reproducible.
在组织学样本制备过程中使用多种染料可以揭示不同的组织特性。然而，由于每种染料的载玻片制备都不同，因此需要在进一步处理之前对组织载玻片进行变形和非刚性配准。由于以下原因，组织学图像的配准变得复杂：（i）组织学图像具有较高的分辨率，（ii）复杂、大、非刚性的变形，（iii）由于使用多种染料，外观上存在差异和部分数据缺失。在这项工作中，我们提出了一种多步骤、自动、非刚性的图像配准方法，专门用于获取多种染色的组织学样本。所提出的方法包括基于特征的仿射配准、彻底的旋转对齐、迭代的、基于强度的仿射配准以及基于与 Demons 算法相结合的模态独立邻域描述符的非刚性配准。提出了一种专用的故障检测机制，使该方法完全自动化，无需任何手动交互。该方法是由 AGH 团队在自动非刚性组织学图像配准（ANHIR）挑战赛中提出的。ANHIR 数据集由 481 对由组织学专家注释的图像对组成。此外，ANHIR 挑战赛的提交结果使用独立的服务器端评估工具进行了评估。主要评估标准是目标配准误差除以图像对角线。中位数目标配准误差低于 0.19%。就中位数目标配准误差的平均排名而言，该方法目前排名第二，与排名第一的方法没有统计学上的显著差异。我们提供方法软件和使用参数的开放访问，使结果完全可重现。
