Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy.
IEEE Trans Biomed Eng. 2011 May;58(5):1421-9. doi: 10.1109/TBME.2011.2106499. Epub 2011 Jan 17.
This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysis.
本研究提出了一种全自动的膜分割技术，用于具有膜染色的免疫组织化学组织图像，这是计算机化免疫组织化学（IHC）中的关键任务。膜分割在免疫组织化学组织图像中特别棘手，因为细胞膜仅在细胞的染色区域可见，而未染色区域不可见。我们的自动化方法提供了染色区域中细胞膜的精确分割，并使用核膜作为空间参考来重建未染色区域的大致位置。准确的逐细胞膜分割允许对每个细胞的形态进行分析，并对目标膜蛋白进行定量，这在癌症特征描述和分类、个性化治疗设计以及任何其他需要细胞形态特征描述的应用等多个医学应用中非常重要。来自不同解剖位置的真实数据集的实验结果证明了我们的方法在 IHC 分析中的广泛适用性和高精度。
