VISILAB, Universidad de Castilla-La Mancha, Spain.
VISILAB, Universidad de Castilla-La Mancha, Spain.
Comput Med Imaging Graph. 2017 Nov;61:14-27. doi: 10.1016/j.compmedimag.2017.06.002. Epub 2017 Jun 13.
Immunohistochemical (IHC) biomarkers in breast tissue microarray (TMA) samples are used daily in pathology departments. In recent years, automatic methods to evaluate positive staining have been investigated since they may save time and reduce errors in the diagnosis. These errors are mostly due to subjective evaluation. The aim of this work is to develop a density tool able to automatically quantify the positive brown IHC stain in breast TMA for different biomarkers. To avoid the problem of colour variation and make a robust tool independent of the staining process, several colour standardization methods have been analysed. Four colour standardization methods have been compared against colour model segmentation. The standardization methods have been compared by means of NBS colour distance. The use of colour standardization helps to reduce noise due to stain and histological sample preparation. However, the most reliable and robust results have been obtained by combining the HSV and RGB colour models for segmentation with the HSB channels. The segmentation provides three outputs based on three saturation values for weak, medium and strong staining. Each output image can be combined according to the type of biomarker staining. The results with 12 biomarkers were evaluated and compared to the segmentation and density calculation done by expert pathologists. The Hausdorff distance, sensitivity and specificity have been used to quantitative validate the results. The tests carried out with 8000 TMA images provided an average of 95.94% accuracy applied to the total tissue cylinder area. Colour standardization was used only when the tissue core had blurring and fading stain and the expert could not evaluate them without a pre-processing.
免疫组织化学（IHC）生物标志物在乳腺组织微阵列（TMA）样本中被病理科医生广泛使用。近年来，人们研究了自动评估阳性染色的方法，因为这些方法可以节省时间并减少诊断中的错误。这些错误主要是由于主观评估造成的。本工作的目的是开发一种密度工具，能够自动量化不同生物标志物的乳腺 TMA 中的阳性棕色 IHC 染色。为了避免颜色变化的问题，并使工具具有鲁棒性且不依赖于染色过程，分析了几种颜色标准化方法。比较了四种颜色标准化方法与颜色模型分割。通过 NBS 颜色距离比较了标准化方法。颜色标准化有助于减少由于染色和组织样本制备引起的噪声。然而，通过将 HSV 和 RGB 颜色模型结合用于分割，并结合 HSB 通道，获得了最可靠和最稳健的结果。分割根据弱、中、强染色的三个饱和度值提供了三个输出。可以根据生物标志物染色的类型组合每个输出图像。使用 12 种生物标志物评估了结果，并与专家病理学家进行的分割和密度计算进行了比较。使用 Hausdorff 距离、灵敏度和特异性对结果进行定量验证。对 8000 张 TMA 图像进行的测试平均准确率为 95.94%，适用于总组织柱面积。仅在组织芯模糊和褪色染色且专家在没有预处理的情况下无法评估它们时才使用颜色标准化。
