School of Computer Science, University of Nottingham, Nottingham, UK.
Mathematics Department, University of Assiut, Assiut, Egypt.
Histopathology. 2019 Jun;74(7):1045-1054. doi: 10.1111/his.13838. Epub 2019 Apr 25.
Immunohistochemistry (IHC) is an essential component of biomarker research in cancer. Automated biomarker quantification is hampered by the failure of computational algorithms to discriminate 'negative' tumour cells from 'negative' stromal cells. We sought to develop an algorithm for segmentation of tumour epithelium in colorectal cancer (CRC), irrespective of the biomarker expression in the cells.
We developed tumour parcellation and quantification (TuPaQ) to segment tumour epithelium and parcellate sections into 'epithelium' and 'non-epithelium'. TuPaQ comprises image pre-processing, extraction of regions of interest (ROIs) and quantification of tumour epithelium (total area occupied by epithelium and number of nuclei in the occupied area). A total of 286 TMA cores from CRC were manually annotated and analysed using the commercial halo software to provide ground truth. The performance of TuPaQ was evaluated against the ground truth using a variety of metrics. The image size of each core was 7000 × 7000 pixels and each core was analysed in a matter of seconds. Pixel × pixel analysis showed a sensitivity of 84% and specificity of 95% in detecting epithelium. The mean tumour area obtained by TuPaQ was very close to the area quantified after manual annotation (r = 0.956, P < 0.001). Moreover, quantification of tumour nuclei by TuPaQ correlated very strongly with that of halo (r = 0.891, P < 0.001).
TuPaQ is a very rapid and accurate method of separating the epithelial and stromal compartments of colorectal tumours. This will allow more accurate and objective analysis of immunohistochemistry.
免疫组织化学（IHC）是癌症生物标志物研究的重要组成部分。计算算法未能区分“阴性”肿瘤细胞与“阴性”基质细胞，这阻碍了自动生物标志物定量。我们试图开发一种用于结直肠癌（CRC）肿瘤上皮分割的算法，而不受细胞中生物标志物表达的影响。
我们开发了肿瘤分割和定量（TuPaQ）来分割肿瘤上皮并将切片分割成“上皮”和“非上皮”。TuPaQ 包括图像预处理、感兴趣区域（ROI）提取和肿瘤上皮定量（上皮占据的总面积和占据区域中的细胞核数）。总共对 286 个 CRC 的 TMA 核心进行了手动注释，并使用商业 halo 软件进行了分析，以提供真实数据。使用各种指标评估 TuPaQ 与真实数据的性能。每个核心的图像大小为 7000×7000 像素，每个核心的分析时间为数秒。像素×像素分析显示，上皮检测的敏感性为 84%，特异性为 95%。TuPaQ 获得的平均肿瘤面积非常接近手动注释后的定量面积（r = 0.956，P < 0.001）。此外，TuPaQ 定量的肿瘤核与 halo 的定量非常相关（r = 0.891，P < 0.001）。
TuPaQ 是一种非常快速和准确的方法，可分离结直肠肿瘤的上皮和基质区室。这将允许更准确和客观地分析免疫组织化学。
