Venet Ludovic, Pati Sarthak, Feldman Michael D, Nasrallah MacLean P, Yushkevich Paul, Bakas Spyridon
Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Appl Sci (Basel). 2021 Feb;11(4). doi: 10.3390/app11041892. Epub 2021 Feb 21.
Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample ( = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions.
组织病理学评估通常能提供有关组织结构和疾病过程的丰富微观信息。然而，所使用的切片非常薄，基本上仅捕捉了特定组织样本的二维图像。对连续切割的二维切片进行准确且稳健的配准，应有助于结合周围三维信息进行更全面的评估。为此，我们在此提出一种两步微分同胚配准方法，该方法将不同染色的组织学切片相互配准，首先进行初始仿射步骤，然后估计变形场。我们在来自自动非刚性组织学图像配准挑战赛的大量（ = 481）且多样的数据上对其进行了定量评估，该方法在比赛中获得了第二名。所得结果表明，所提出的方法能够稳健地（平均稳健性 = 0.9898）且准确地（平均相对目标配准误差 = 0.2%）配准不同解剖部位的不同染色组织学切片，同时保持合理的计算效率（每次配准<1分钟）。该方法是通过改编一种为三维射线扫描设计的通用配准算法而开发的，并且在配准高分辨率二维组织学图像时始终能获得准确的结果。组织学图像的准确配准有助于更好地理解细胞、血管、基质、神经和免疫细胞相互作用的空间排列和生长模式。
