Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan.
PLoS One. 2011 Feb 28;6(2):e15818. doi: 10.1371/journal.pone.0015818.
Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.
组织微阵列（TMA）是一种高通量分析工具，可用于鉴定人类癌症中的新诊断和预后标志物。然而，在常规组织化学和免疫组织化学（IHC）图像上进行肿瘤检测的标准自动化方法还不够完善。本文提出了一种强大的自动化肿瘤细胞分割模型，该模型可应用于常规组织化学组织切片和 IHC 切片，并与现有方法的基于斑点或区域的分割相比，处理更精细的像素级分割。该技术大大提高了 TMA 构建过程，并在生物标志物分析中的自动化 IHC 定量中发挥了重要作用，因为排除基质区域至关重要。通过最精细的像素级评估（而不是基于区域或基于对象的评估），实验结果表明，该方法在两种不同类型的病理虚拟幻灯片（即常规组织化学 H&E 和 IHC 图像）中分别能够达到 80%和 78%的准确率。该技术大大减少了病理学家的劳动密集型工作量，并极大地加快了 TMA 构建的过程，并为全自动 IHC 定量提供了可能性。
