Ventana Medical Systems, Inc. A Member of the Roche Group, 94043, USA.
Ventana Medical Systems, Inc. A Member of the Roche Group, 94043, USA.
Comput Med Imaging Graph. 2015 Dec;46 Pt 1:30-39. doi: 10.1016/j.compmedimag.2015.04.001. Epub 2015 Apr 15.
Multiplex immunohistochemistry (IHC) staining is a new, emerging technique for the detection of multiple biomarkers within a single tissue section. The initial key step in multiplex IHC image analysis in digital pathology is of tremendous clinical importance due to its ability to accurately unmix the IHC image and differentiate each of the stains. The technique has become popular due to its significant efficiency and the rich diagnostic information it contains. The intriguing task of unmixing a three-channel CCD color camera acquired RGB image into more than three colors is very challenging, and to the best of our knowledge, hardly studied in academic literature. This paper presents a novel stain unmixing algorithm for brightfield multiplex IHC images based on a group sparsity model. The proposed framework achieves robust unmixing for more than three chromogenic dyes while preserving the biological constraints of the biomarkers. Typically, a number of biomarkers co-localize in the same cell parts named priori. With this biological information in mind, the number of stains at one pixel therefore has a fixed up-bound, i.e. equivalent to the number of co-localized biomarkers. By leveraging the group sparsity model, the fractions of stain contributions from the co-localized biomarkers are explicitly modeled into one group to yield the least square solution within the group. A sparse solution is obtained among the groups since ideally only one group of biomarkers is present at each pixel. The algorithm is evaluated on both synthetic and clinical data sets, and demonstrates better unmixing results than the existing strategies.
多重免疫组化（IHC）染色是一种新兴的技术，用于在单个组织切片中检测多个生物标志物。在数字病理学中，多重 IHC 图像分析的初始关键步骤具有巨大的临床重要性，因为它能够准确地混合 IHC 图像并区分每个染色。该技术因其显著的效率和包含的丰富诊断信息而广受欢迎。将三通道 CCD 彩色相机获取的 RGB 图像混合成三种以上颜色的混合任务非常具有挑战性，而且在学术文献中几乎没有研究。本文提出了一种基于群组稀疏模型的明场多重 IHC 图像的新染色混合算法。该框架在保留生物标志物的生物学约束的同时，实现了对三种以上显色染料的稳健混合。通常，许多生物标志物在同一细胞部分（称为先验）中共同定位。考虑到这些生物学信息，因此一个像素处的染色数量有一个固定的上限，即等于共同定位的生物标志物的数量。通过利用群组稀疏模型，将共同定位的生物标志物的染色贡献分数明确建模到一个群组中，以在群组内获得最小二乘解。由于理想情况下每个像素仅存在一组生物标志物，因此在群组之间获得稀疏解。该算法在合成和临床数据集上进行了评估，结果表明比现有策略具有更好的混合效果。
