Liu Pangpang, Wang Fusheng, Teodoro George, Kong Jun
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, 30303, USA.
Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794, USA.
Proc IEEE Int Symp Biomed Imaging. 2021 Apr;2021:1827-1830. doi: 10.1109/isbi48211.2021.9434114. Epub 2021 May 25.
Three-dimensional (3D) digital pathology has been emerging for next-generation tissue based cancer research. To enable such histopathology image volume analysis, serial histopathology slides need to be well aligned. In this paper, we propose a histopathology image registration fine tuning method with integrated landmark evaluations by texture and spatial proximity measures. Representative anatomical structures and image corner features are first detected as landmark candidates. Next, we identify strong and modify weak matched landmarks by leveraging image texture features and landmark spatial proximity measures. Both qualitative and quantitative results of extensive experiments demonstrate that our proposed method is robust and can further enhance registration accuracy of our previously registered image set by 31.15% (correlation), 4.88% (mutual information), and 41.02% (mean squared error), respectively. The promising experimental results suggest that our method can be used as a fine tuning module to further boost registration accuracy, a premise of histology spatial and morphology analysis in an information-lossless 3D tissue space for cancer research.
三维（3D）数字病理学正在兴起，用于下一代基于组织的癌症研究。为了实现这种组织病理学图像体积分析，需要对连续的组织病理学切片进行良好对齐。在本文中，我们提出了一种组织病理学图像配准微调方法，该方法通过纹理和空间接近度测量对地标进行综合评估。首先将具有代表性的解剖结构和图像角点特征检测为地标候选点。接下来，我们利用图像纹理特征和地标空间接近度测量来识别强匹配地标并修正弱匹配地标。大量实验的定性和定量结果均表明，我们提出的方法具有鲁棒性，并且能够分别将我们之前配准的图像集的配准精度进一步提高31.15%（相关性）、4.88%（互信息）和41.02%（均方误差）。这些有前景的实验结果表明，我们的方法可以用作微调模块，以进一步提高配准精度，这是在用于癌症研究的信息无损3D组织空间中进行组织学空间和形态分析的前提。
