Human epidermal growth factor receptor 2 (HER2) is one of the widely used Immunohistochemical (IHC) markers for prognostic evaluation amongst the patient of breast cancer. Accurate quantification of cell membrane is essential for HER2 scoring in therapeutic decision making. In modern laboratory practice, expert pathologist visually assesses the HER2‐stained tissue sample under the bright field microscope for cell membrane assessment. This manual assessment is time consuming, tedious and quite often results in interobserver variability. Further, the burden of increasing number of patients is a challenge for the pathologists. To address these challenges, there is an urgent need with a rapid HER2 cell membrane extraction method. The proposed study aims at developing an automated IHC scoring system, termed as AutoIHC‐Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. A series of image processing approaches have been used to automatically extract the stained cells and membrane region, followed by automatic assessment of complete and broken membrane. Finally, a set of features are used to automatically classify the tissue under observation for the quantitative scoring as 0/1+, 2+ and 3+. In a set of surgically extracted cases of HER2‐stained tissues, obtained from collaborative hospital for the testing and validation of the proposed approach AutoIHC‐Analyzer and publicly available open source ImmunoMembrane software are compared for 90 set of randomly acquired images with the scores by expert pathologist where significant correlation is observed [(r = 0.9448; p < 0.001) and (r = 0.8521; p < 0.001)] respectively. The output shows promising quantification in automated scoring.

中文翻译：

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AutoIHC-Analyzer：用于 HER2 分子标记中自动膜提取/评分的计算机辅助显微镜

人表皮生长因子受体 2 (HER2) 是广泛使用的免疫组织化学 (IHC) 标志物之一，用于乳腺癌患者的预后评估。细胞膜的准确定量对于治疗决策中的 HER2 评分至关重要。在现代实验室实践中，专家病理学家在明场显微镜下目视评估 HER2 染色的组织样本以进行细胞膜评估。这种手动评估既费时又乏味，而且经常导致观察者之间的差异。此外，增加患者数量的负担对病理学家来说是一个挑战。为了应对这些挑战，迫切需要一种快速的 HER2 细胞膜提取方法。拟议的研究旨在开发一种称为 AutoIHC-Analyzer 的自动化 IHC 评分系统，用于自动细胞膜提取，然后从染色组织图像中评估 HER2 分子表达。一系列图像处理方法已被用于自动提取染色细胞和膜区域，然后自动评估完整和破损的膜。最后，使用一组特征自动将被观察组织分类为0/1+、2+和3+。在一组手术提取的 HER2 染色组织病例中，从合作医院获得，用于测试和验证所提出的方法 AutoIHC-Analyzer 和公开可用的开源 ImmunoMembrane 软件对 90 组随机获取的图像与专家评分进行比较观察到显着相关性的病理学家 [(r = 0.9448; p < 0.001) 和 (r = 0.8521; p < 0.001)] 分别。输出显示在自动评分方面有希望的量化。